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18,800 | sorgerlab/indra | indra/sources/hprd/processor.py | HprdProcessor.get_complexes | def get_complexes(self, cplx_df):
"""Generate Complex Statements from the HPRD protein complexes data.
Parameters
----------
cplx_df : pandas.DataFrame
DataFrame loaded from the PROTEIN_COMPLEXES.txt file.
"""
# Group the agents for the complex
logger.info('Processing complexes...')
for cplx_id, this_cplx in cplx_df.groupby('CPLX_ID'):
agents = []
for hprd_id in this_cplx.HPRD_ID:
ag = self._make_agent(hprd_id)
if ag is not None:
agents.append(ag)
# Make sure we got some agents!
if not agents:
continue
# Get evidence info from first member of complex
row0 = this_cplx.iloc[0]
isoform_id = '%s_1' % row0.HPRD_ID
ev_list = self._get_evidence(row0.HPRD_ID, isoform_id, row0.PMIDS,
row0.EVIDENCE, 'interactions')
stmt = Complex(agents, evidence=ev_list)
self.statements.append(stmt) | python | def get_complexes(self, cplx_df):
# Group the agents for the complex
logger.info('Processing complexes...')
for cplx_id, this_cplx in cplx_df.groupby('CPLX_ID'):
agents = []
for hprd_id in this_cplx.HPRD_ID:
ag = self._make_agent(hprd_id)
if ag is not None:
agents.append(ag)
# Make sure we got some agents!
if not agents:
continue
# Get evidence info from first member of complex
row0 = this_cplx.iloc[0]
isoform_id = '%s_1' % row0.HPRD_ID
ev_list = self._get_evidence(row0.HPRD_ID, isoform_id, row0.PMIDS,
row0.EVIDENCE, 'interactions')
stmt = Complex(agents, evidence=ev_list)
self.statements.append(stmt) | [
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Parameters
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cplx_df : pandas.DataFrame
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18,801 | sorgerlab/indra | indra/sources/hprd/processor.py | HprdProcessor.get_ptms | def get_ptms(self, ptm_df):
"""Generate Modification statements from the HPRD PTM data.
Parameters
----------
ptm_df : pandas.DataFrame
DataFrame loaded from the POST_TRANSLATIONAL_MODIFICATIONS.txt file.
"""
logger.info('Processing PTMs...')
# Iterate over the rows of the dataframe
for ix, row in ptm_df.iterrows():
# Check the modification type; if we can't make an INDRA statement
# for it, then skip it
ptm_class = _ptm_map[row['MOD_TYPE']]
if ptm_class is None:
continue
# Use the Refseq protein ID for the substrate to make sure that
# we get the right Uniprot ID for the isoform
sub_ag = self._make_agent(row['HPRD_ID'],
refseq_id=row['REFSEQ_PROTEIN'])
# If we couldn't get the substrate, skip the statement
if sub_ag is None:
continue
enz_id = _nan_to_none(row['ENZ_HPRD_ID'])
enz_ag = self._make_agent(enz_id)
res = _nan_to_none(row['RESIDUE'])
pos = _nan_to_none(row['POSITION'])
if pos is not None and ';' in pos:
pos, dash = pos.split(';')
assert dash == '-'
# As a fallback for later site mapping, we also get the protein
# sequence information in case there was a problem with the
# RefSeq->Uniprot mapping
assert res
assert pos
motif_dict = self._get_seq_motif(row['REFSEQ_PROTEIN'], res, pos)
# Get evidence
ev_list = self._get_evidence(
row['HPRD_ID'], row['HPRD_ISOFORM'], row['PMIDS'],
row['EVIDENCE'], 'ptms', motif_dict)
stmt = ptm_class(enz_ag, sub_ag, res, pos, evidence=ev_list)
self.statements.append(stmt) | python | def get_ptms(self, ptm_df):
logger.info('Processing PTMs...')
# Iterate over the rows of the dataframe
for ix, row in ptm_df.iterrows():
# Check the modification type; if we can't make an INDRA statement
# for it, then skip it
ptm_class = _ptm_map[row['MOD_TYPE']]
if ptm_class is None:
continue
# Use the Refseq protein ID for the substrate to make sure that
# we get the right Uniprot ID for the isoform
sub_ag = self._make_agent(row['HPRD_ID'],
refseq_id=row['REFSEQ_PROTEIN'])
# If we couldn't get the substrate, skip the statement
if sub_ag is None:
continue
enz_id = _nan_to_none(row['ENZ_HPRD_ID'])
enz_ag = self._make_agent(enz_id)
res = _nan_to_none(row['RESIDUE'])
pos = _nan_to_none(row['POSITION'])
if pos is not None and ';' in pos:
pos, dash = pos.split(';')
assert dash == '-'
# As a fallback for later site mapping, we also get the protein
# sequence information in case there was a problem with the
# RefSeq->Uniprot mapping
assert res
assert pos
motif_dict = self._get_seq_motif(row['REFSEQ_PROTEIN'], res, pos)
# Get evidence
ev_list = self._get_evidence(
row['HPRD_ID'], row['HPRD_ISOFORM'], row['PMIDS'],
row['EVIDENCE'], 'ptms', motif_dict)
stmt = ptm_class(enz_ag, sub_ag, res, pos, evidence=ev_list)
self.statements.append(stmt) | [
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ptm_df : pandas.DataFrame
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18,802 | sorgerlab/indra | indra/sources/hprd/processor.py | HprdProcessor.get_ppis | def get_ppis(self, ppi_df):
"""Generate Complex Statements from the HPRD PPI data.
Parameters
----------
ppi_df : pandas.DataFrame
DataFrame loaded from the BINARY_PROTEIN_PROTEIN_INTERACTIONS.txt
file.
"""
logger.info('Processing PPIs...')
for ix, row in ppi_df.iterrows():
agA = self._make_agent(row['HPRD_ID_A'])
agB = self._make_agent(row['HPRD_ID_B'])
# If don't get valid agents for both, skip this PPI
if agA is None or agB is None:
continue
isoform_id = '%s_1' % row['HPRD_ID_A']
ev_list = self._get_evidence(
row['HPRD_ID_A'], isoform_id, row['PMIDS'],
row['EVIDENCE'], 'interactions')
stmt = Complex([agA, agB], evidence=ev_list)
self.statements.append(stmt) | python | def get_ppis(self, ppi_df):
logger.info('Processing PPIs...')
for ix, row in ppi_df.iterrows():
agA = self._make_agent(row['HPRD_ID_A'])
agB = self._make_agent(row['HPRD_ID_B'])
# If don't get valid agents for both, skip this PPI
if agA is None or agB is None:
continue
isoform_id = '%s_1' % row['HPRD_ID_A']
ev_list = self._get_evidence(
row['HPRD_ID_A'], isoform_id, row['PMIDS'],
row['EVIDENCE'], 'interactions')
stmt = Complex([agA, agB], evidence=ev_list)
self.statements.append(stmt) | [
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18,803 | sorgerlab/indra | indra/sources/isi/processor.py | _build_verb_statement_mapping | def _build_verb_statement_mapping():
"""Build the mapping between ISI verb strings and INDRA statement classes.
Looks up the INDRA statement class name, if any, in a resource file,
and resolves this class name to a class.
Returns
-------
verb_to_statement_type : dict
Dictionary mapping verb name to an INDRA statment class
"""
path_this = os.path.dirname(os.path.abspath(__file__))
map_path = os.path.join(path_this, 'isi_verb_to_indra_statement_type.tsv')
with open(map_path, 'r') as f:
first_line = True
verb_to_statement_type = {}
for line in f:
if not first_line:
line = line[:-1]
tokens = line.split('\t')
if len(tokens) == 2 and len(tokens[1]) > 0:
verb = tokens[0]
s_type = tokens[1]
try:
statement_class = getattr(ist, s_type)
verb_to_statement_type[verb] = statement_class
except Exception:
pass
else:
first_line = False
return verb_to_statement_type | python | def _build_verb_statement_mapping():
path_this = os.path.dirname(os.path.abspath(__file__))
map_path = os.path.join(path_this, 'isi_verb_to_indra_statement_type.tsv')
with open(map_path, 'r') as f:
first_line = True
verb_to_statement_type = {}
for line in f:
if not first_line:
line = line[:-1]
tokens = line.split('\t')
if len(tokens) == 2 and len(tokens[1]) > 0:
verb = tokens[0]
s_type = tokens[1]
try:
statement_class = getattr(ist, s_type)
verb_to_statement_type[verb] = statement_class
except Exception:
pass
else:
first_line = False
return verb_to_statement_type | [
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18,804 | sorgerlab/indra | indra/sources/isi/processor.py | IsiProcessor.get_statements | def get_statements(self):
"""Process reader output to produce INDRA Statements."""
for k, v in self.reader_output.items():
for interaction in v['interactions']:
self._process_interaction(k, interaction, v['text'], self.pmid,
self.extra_annotations) | python | def get_statements(self):
for k, v in self.reader_output.items():
for interaction in v['interactions']:
self._process_interaction(k, interaction, v['text'], self.pmid,
self.extra_annotations) | [
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18,805 | sorgerlab/indra | indra/sources/isi/processor.py | IsiProcessor._process_interaction | def _process_interaction(self, source_id, interaction, text, pmid,
extra_annotations):
"""Process an interaction JSON tuple from the ISI output, and adds up
to one statement to the list of extracted statements.
Parameters
----------
source_id : str
the JSON key corresponding to the sentence in the ISI output
interaction: the JSON list with subject/verb/object information
about the event in the ISI output
text : str
the text of the sentence
pmid : str
the PMID of the article from which the information was extracted
extra_annotations : dict
Additional annotations to add to the statement's evidence,
potentially containing metadata about the source. Annotations
with the key "interaction" will be overridden by the JSON
interaction tuple from the ISI output
"""
verb = interaction[0].lower()
subj = interaction[-2]
obj = interaction[-1]
# Make ungrounded agent objects for the subject and object
# Grounding will happen after all statements are extracted in __init__
subj = self._make_agent(subj)
obj = self._make_agent(obj)
# Make an evidence object
annotations = deepcopy(extra_annotations)
if 'interaction' in extra_annotations:
logger.warning("'interaction' key of extra_annotations ignored" +
" since this is reserved for storing the raw ISI " +
"input.")
annotations['source_id'] = source_id
annotations['interaction'] = interaction
ev = ist.Evidence(source_api='isi',
pmid=pmid,
text=text.rstrip(),
annotations=annotations)
# For binding time interactions, it is said that a catayst might be
# specified. We don't use this for now, but extract in case we want
# to in the future
cataylst_specified = False
if len(interaction) == 4:
catalyst = interaction[1]
if catalyst is not None:
cataylst_specified = True
self.verbs.add(verb)
statement = None
if verb in verb_to_statement_type:
statement_class = verb_to_statement_type[verb]
if statement_class == ist.Complex:
statement = ist.Complex([subj, obj], evidence=ev)
else:
statement = statement_class(subj, obj, evidence=ev)
if statement is not None:
# For Complex statements, the ISI reader produces two events:
# binds(A, B) and binds(B, A)
# We want only one Complex statement for each sentence, so check
# to see if we already have a Complex for this source_id with the
# same members
already_have = False
if type(statement) == ist.Complex:
for old_s in self.statements:
old_id = statement.evidence[0].source_id
new_id = old_s.evidence[0].source_id
if type(old_s) == ist.Complex and old_id == new_id:
old_statement_members = \
[m.db_refs['TEXT'] for m in old_s.members]
old_statement_members = sorted(old_statement_members)
new_statement_members = [m.db_refs['TEXT']
for m in statement.members]
new_statement_members = sorted(new_statement_members)
if old_statement_members == new_statement_members:
already_have = True
break
if not already_have:
self.statements.append(statement) | python | def _process_interaction(self, source_id, interaction, text, pmid,
extra_annotations):
verb = interaction[0].lower()
subj = interaction[-2]
obj = interaction[-1]
# Make ungrounded agent objects for the subject and object
# Grounding will happen after all statements are extracted in __init__
subj = self._make_agent(subj)
obj = self._make_agent(obj)
# Make an evidence object
annotations = deepcopy(extra_annotations)
if 'interaction' in extra_annotations:
logger.warning("'interaction' key of extra_annotations ignored" +
" since this is reserved for storing the raw ISI " +
"input.")
annotations['source_id'] = source_id
annotations['interaction'] = interaction
ev = ist.Evidence(source_api='isi',
pmid=pmid,
text=text.rstrip(),
annotations=annotations)
# For binding time interactions, it is said that a catayst might be
# specified. We don't use this for now, but extract in case we want
# to in the future
cataylst_specified = False
if len(interaction) == 4:
catalyst = interaction[1]
if catalyst is not None:
cataylst_specified = True
self.verbs.add(verb)
statement = None
if verb in verb_to_statement_type:
statement_class = verb_to_statement_type[verb]
if statement_class == ist.Complex:
statement = ist.Complex([subj, obj], evidence=ev)
else:
statement = statement_class(subj, obj, evidence=ev)
if statement is not None:
# For Complex statements, the ISI reader produces two events:
# binds(A, B) and binds(B, A)
# We want only one Complex statement for each sentence, so check
# to see if we already have a Complex for this source_id with the
# same members
already_have = False
if type(statement) == ist.Complex:
for old_s in self.statements:
old_id = statement.evidence[0].source_id
new_id = old_s.evidence[0].source_id
if type(old_s) == ist.Complex and old_id == new_id:
old_statement_members = \
[m.db_refs['TEXT'] for m in old_s.members]
old_statement_members = sorted(old_statement_members)
new_statement_members = [m.db_refs['TEXT']
for m in statement.members]
new_statement_members = sorted(new_statement_members)
if old_statement_members == new_statement_members:
already_have = True
break
if not already_have:
self.statements.append(statement) | [
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the text of the sentence
pmid : str
the PMID of the article from which the information was extracted
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Additional annotations to add to the statement's evidence,
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18,806 | sorgerlab/indra | indra/sources/geneways/actionmention_parser.py | GenewaysActionMention.make_annotation | def make_annotation(self):
"""Returns a dictionary with all properties of the action mention."""
annotation = dict()
# Put all properties of the action object into the annotation
for item in dir(self):
if len(item) > 0 and item[0] != '_' and \
not inspect.ismethod(getattr(self, item)):
annotation[item] = getattr(self, item)
return annotation | python | def make_annotation(self):
annotation = dict()
# Put all properties of the action object into the annotation
for item in dir(self):
if len(item) > 0 and item[0] != '_' and \
not inspect.ismethod(getattr(self, item)):
annotation[item] = getattr(self, item)
return annotation | [
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18,807 | sorgerlab/indra | indra/sources/biopax/processor.py | _match_to_array | def _match_to_array(m):
""" Returns an array consisting of the elements obtained from a pattern
search cast into their appropriate classes. """
return [_cast_biopax_element(m.get(i)) for i in range(m.varSize())] | python | def _match_to_array(m):
return [_cast_biopax_element(m.get(i)) for i in range(m.varSize())] | [
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18,808 | sorgerlab/indra | indra/sources/biopax/processor.py | _is_complex | def _is_complex(pe):
"""Return True if the physical entity is a complex"""
val = isinstance(pe, _bp('Complex')) or \
isinstance(pe, _bpimpl('Complex'))
return val | python | def _is_complex(pe):
val = isinstance(pe, _bp('Complex')) or \
isinstance(pe, _bpimpl('Complex'))
return val | [
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18,809 | sorgerlab/indra | indra/sources/biopax/processor.py | _is_protein | def _is_protein(pe):
"""Return True if the element is a protein"""
val = isinstance(pe, _bp('Protein')) or \
isinstance(pe, _bpimpl('Protein')) or \
isinstance(pe, _bp('ProteinReference')) or \
isinstance(pe, _bpimpl('ProteinReference'))
return val | python | def _is_protein(pe):
val = isinstance(pe, _bp('Protein')) or \
isinstance(pe, _bpimpl('Protein')) or \
isinstance(pe, _bp('ProteinReference')) or \
isinstance(pe, _bpimpl('ProteinReference'))
return val | [
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18,810 | sorgerlab/indra | indra/sources/biopax/processor.py | _is_rna | def _is_rna(pe):
"""Return True if the element is an RNA"""
val = isinstance(pe, _bp('Rna')) or isinstance(pe, _bpimpl('Rna'))
return val | python | def _is_rna(pe):
val = isinstance(pe, _bp('Rna')) or isinstance(pe, _bpimpl('Rna'))
return val | [
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18,811 | sorgerlab/indra | indra/sources/biopax/processor.py | _is_small_molecule | def _is_small_molecule(pe):
"""Return True if the element is a small molecule"""
val = isinstance(pe, _bp('SmallMolecule')) or \
isinstance(pe, _bpimpl('SmallMolecule')) or \
isinstance(pe, _bp('SmallMoleculeReference')) or \
isinstance(pe, _bpimpl('SmallMoleculeReference'))
return val | python | def _is_small_molecule(pe):
val = isinstance(pe, _bp('SmallMolecule')) or \
isinstance(pe, _bpimpl('SmallMolecule')) or \
isinstance(pe, _bp('SmallMoleculeReference')) or \
isinstance(pe, _bpimpl('SmallMoleculeReference'))
return val | [
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18,812 | sorgerlab/indra | indra/sources/biopax/processor.py | _is_physical_entity | def _is_physical_entity(pe):
"""Return True if the element is a physical entity"""
val = isinstance(pe, _bp('PhysicalEntity')) or \
isinstance(pe, _bpimpl('PhysicalEntity'))
return val | python | def _is_physical_entity(pe):
val = isinstance(pe, _bp('PhysicalEntity')) or \
isinstance(pe, _bpimpl('PhysicalEntity'))
return val | [
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18,813 | sorgerlab/indra | indra/sources/biopax/processor.py | _is_modification_or_activity | def _is_modification_or_activity(feature):
"""Return True if the feature is a modification"""
if not (isinstance(feature, _bp('ModificationFeature')) or \
isinstance(feature, _bpimpl('ModificationFeature'))):
return None
mf_type = feature.getModificationType()
if mf_type is None:
return None
mf_type_terms = mf_type.getTerm().toArray()
for term in mf_type_terms:
if term in ('residue modification, active',
'residue modification, inactive',
'active', 'inactive'):
return 'activity'
return 'modification' | python | def _is_modification_or_activity(feature):
if not (isinstance(feature, _bp('ModificationFeature')) or \
isinstance(feature, _bpimpl('ModificationFeature'))):
return None
mf_type = feature.getModificationType()
if mf_type is None:
return None
mf_type_terms = mf_type.getTerm().toArray()
for term in mf_type_terms:
if term in ('residue modification, active',
'residue modification, inactive',
'active', 'inactive'):
return 'activity'
return 'modification' | [
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18,814 | sorgerlab/indra | indra/sources/biopax/processor.py | _is_reference | def _is_reference(bpe):
"""Return True if the element is an entity reference."""
if isinstance(bpe, _bp('ProteinReference')) or \
isinstance(bpe, _bpimpl('ProteinReference')) or \
isinstance(bpe, _bp('SmallMoleculeReference')) or \
isinstance(bpe, _bpimpl('SmallMoleculeReference')) or \
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isinstance(bpe, _bpimpl('RnaReference')) or \
isinstance(bpe, _bp('EntityReference')) or \
isinstance(bpe, _bpimpl('EntityReference')):
return True
else:
return False | python | def _is_reference(bpe):
if isinstance(bpe, _bp('ProteinReference')) or \
isinstance(bpe, _bpimpl('ProteinReference')) or \
isinstance(bpe, _bp('SmallMoleculeReference')) or \
isinstance(bpe, _bpimpl('SmallMoleculeReference')) or \
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return True
else:
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18,815 | sorgerlab/indra | indra/sources/biopax/processor.py | _is_entity | def _is_entity(bpe):
"""Return True if the element is a physical entity."""
if isinstance(bpe, _bp('Protein')) or \
isinstance(bpe, _bpimpl('Protein')) or \
isinstance(bpe, _bp('SmallMolecule')) or \
isinstance(bpe, _bpimpl('SmallMolecule')) or \
isinstance(bpe, _bp('Complex')) or \
isinstance(bpe, _bpimpl('Complex')) or \
isinstance(bpe, _bp('Rna')) or \
isinstance(bpe, _bpimpl('Rna')) or \
isinstance(bpe, _bp('RnaRegion')) or \
isinstance(bpe, _bpimpl('RnaRegion')) or \
isinstance(bpe, _bp('DnaRegion')) or \
isinstance(bpe, _bpimpl('DnaRegion')) or \
isinstance(bpe, _bp('PhysicalEntity')) or \
isinstance(bpe, _bpimpl('PhysicalEntity')):
return True
else:
return False | python | def _is_entity(bpe):
if isinstance(bpe, _bp('Protein')) or \
isinstance(bpe, _bpimpl('Protein')) or \
isinstance(bpe, _bp('SmallMolecule')) or \
isinstance(bpe, _bpimpl('SmallMolecule')) or \
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isinstance(bpe, _bpimpl('Rna')) or \
isinstance(bpe, _bp('RnaRegion')) or \
isinstance(bpe, _bpimpl('RnaRegion')) or \
isinstance(bpe, _bp('DnaRegion')) or \
isinstance(bpe, _bpimpl('DnaRegion')) or \
isinstance(bpe, _bp('PhysicalEntity')) or \
isinstance(bpe, _bpimpl('PhysicalEntity')):
return True
else:
return False | [
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18,816 | sorgerlab/indra | indra/sources/biopax/processor.py | _is_catalysis | def _is_catalysis(bpe):
"""Return True if the element is Catalysis."""
if isinstance(bpe, _bp('Catalysis')) or \
isinstance(bpe, _bpimpl('Catalysis')):
return True
else:
return False | python | def _is_catalysis(bpe):
if isinstance(bpe, _bp('Catalysis')) or \
isinstance(bpe, _bpimpl('Catalysis')):
return True
else:
return False | [
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18,817 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor.print_statements | def print_statements(self):
"""Print all INDRA Statements collected by the processors."""
for i, stmt in enumerate(self.statements):
print("%s: %s" % (i, stmt)) | python | def print_statements(self):
for i, stmt in enumerate(self.statements):
print("%s: %s" % (i, stmt)) | [
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18,818 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor.save_model | def save_model(self, file_name=None):
"""Save the BioPAX model object in an OWL file.
Parameters
----------
file_name : Optional[str]
The name of the OWL file to save the model in.
"""
if file_name is None:
logger.error('Missing file name')
return
pcc.model_to_owl(self.model, file_name) | python | def save_model(self, file_name=None):
if file_name is None:
logger.error('Missing file name')
return
pcc.model_to_owl(self.model, file_name) | [
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18,819 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor.eliminate_exact_duplicates | def eliminate_exact_duplicates(self):
"""Eliminate Statements that were extracted multiple times.
Due to the way the patterns are implemented, they can sometimes yield
the same Statement information multiple times, in which case,
we end up with redundant Statements that aren't from independent
underlying entries. To avoid this, here, we filter out such
duplicates.
"""
# Here we use the deep hash of each Statement, and by making a dict,
# we effectively keep only one Statement with a given deep hash
self.statements = list({stmt.get_hash(shallow=False, refresh=True): stmt
for stmt in self.statements}.values()) | python | def eliminate_exact_duplicates(self):
# Here we use the deep hash of each Statement, and by making a dict,
# we effectively keep only one Statement with a given deep hash
self.statements = list({stmt.get_hash(shallow=False, refresh=True): stmt
for stmt in self.statements}.values()) | [
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18,820 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor.get_complexes | def get_complexes(self):
"""Extract INDRA Complex Statements from the BioPAX model.
This method searches for org.biopax.paxtools.model.level3.Complex
objects which represent molecular complexes. It doesn't reuse
BioPAX Pattern's org.biopax.paxtools.pattern.PatternBox.inComplexWith
query since that retrieves pairs of complex members rather than
the full complex.
"""
for obj in self.model.getObjects().toArray():
bpe = _cast_biopax_element(obj)
if not _is_complex(bpe):
continue
ev = self._get_evidence(bpe)
members = self._get_complex_members(bpe)
if members is not None:
if len(members) > 10:
logger.debug('Skipping complex with more than 10 members.')
continue
complexes = _get_combinations(members)
for c in complexes:
self.statements.append(decode_obj(Complex(c, ev),
encoding='utf-8')) | python | def get_complexes(self):
for obj in self.model.getObjects().toArray():
bpe = _cast_biopax_element(obj)
if not _is_complex(bpe):
continue
ev = self._get_evidence(bpe)
members = self._get_complex_members(bpe)
if members is not None:
if len(members) > 10:
logger.debug('Skipping complex with more than 10 members.')
continue
complexes = _get_combinations(members)
for c in complexes:
self.statements.append(decode_obj(Complex(c, ev),
encoding='utf-8')) | [
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18,821 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor.get_modifications | def get_modifications(self):
"""Extract INDRA Modification Statements from the BioPAX model.
To extract Modifications, this method reuses the structure of
BioPAX Pattern's
org.biopax.paxtools.pattern.PatternBox.constrolsStateChange pattern
with additional constraints to specify the type of state change
occurring (phosphorylation, deubiquitination, etc.).
"""
for modtype, modclass in modtype_to_modclass.items():
# TODO: we could possibly try to also extract generic
# modifications here
if modtype == 'modification':
continue
stmts = self._get_generic_modification(modclass)
self.statements += stmts | python | def get_modifications(self):
for modtype, modclass in modtype_to_modclass.items():
# TODO: we could possibly try to also extract generic
# modifications here
if modtype == 'modification':
continue
stmts = self._get_generic_modification(modclass)
self.statements += stmts | [
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18,822 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor.get_activity_modification | def get_activity_modification(self):
"""Extract INDRA ActiveForm statements from the BioPAX model.
This method extracts ActiveForm Statements that are due to
protein modifications. This method reuses the structure of
BioPAX Pattern's
org.biopax.paxtools.pattern.PatternBox.constrolsStateChange pattern
with additional constraints to specify the gain or loss of a
modification occurring (phosphorylation, deubiquitination, etc.)
and the gain or loss of activity due to the modification state
change.
"""
mod_filter = 'residue modification, active'
for is_active in [True, False]:
p = self._construct_modification_pattern()
rel = mcct.GAIN if is_active else mcct.LOSS
p.add(mcc(rel, mod_filter),
"input simple PE", "output simple PE")
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
for r in res_array:
reaction = r[p.indexOf('Conversion')]
activity = 'activity'
input_spe = r[p.indexOf('input simple PE')]
output_spe = r[p.indexOf('output simple PE')]
# Get the modifications
mod_in = \
BiopaxProcessor._get_entity_mods(input_spe)
mod_out = \
BiopaxProcessor._get_entity_mods(output_spe)
mod_shared = _get_mod_intersection(mod_in, mod_out)
gained_mods = _get_mod_difference(mod_out, mod_in)
# Here we get the evidence for the BiochemicalReaction
ev = self._get_evidence(reaction)
agents = self._get_agents_from_entity(output_spe)
for agent in _listify(agents):
static_mods = _get_mod_difference(agent.mods,
gained_mods)
# NOTE: with the ActiveForm representation we cannot
# separate static_mods and gained_mods. We assume here
# that the static_mods are inconsequential and therefore
# are not mentioned as an Agent condition, following
# don't care don't write semantics. Therefore only the
# gained_mods are listed in the ActiveForm as Agent
# conditions.
if gained_mods:
agent.mods = gained_mods
stmt = ActiveForm(agent, activity, is_active,
evidence=ev)
self.statements.append(decode_obj(stmt,
encoding='utf-8')) | python | def get_activity_modification(self):
mod_filter = 'residue modification, active'
for is_active in [True, False]:
p = self._construct_modification_pattern()
rel = mcct.GAIN if is_active else mcct.LOSS
p.add(mcc(rel, mod_filter),
"input simple PE", "output simple PE")
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
for r in res_array:
reaction = r[p.indexOf('Conversion')]
activity = 'activity'
input_spe = r[p.indexOf('input simple PE')]
output_spe = r[p.indexOf('output simple PE')]
# Get the modifications
mod_in = \
BiopaxProcessor._get_entity_mods(input_spe)
mod_out = \
BiopaxProcessor._get_entity_mods(output_spe)
mod_shared = _get_mod_intersection(mod_in, mod_out)
gained_mods = _get_mod_difference(mod_out, mod_in)
# Here we get the evidence for the BiochemicalReaction
ev = self._get_evidence(reaction)
agents = self._get_agents_from_entity(output_spe)
for agent in _listify(agents):
static_mods = _get_mod_difference(agent.mods,
gained_mods)
# NOTE: with the ActiveForm representation we cannot
# separate static_mods and gained_mods. We assume here
# that the static_mods are inconsequential and therefore
# are not mentioned as an Agent condition, following
# don't care don't write semantics. Therefore only the
# gained_mods are listed in the ActiveForm as Agent
# conditions.
if gained_mods:
agent.mods = gained_mods
stmt = ActiveForm(agent, activity, is_active,
evidence=ev)
self.statements.append(decode_obj(stmt,
encoding='utf-8')) | [
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"mcct",... | Extract INDRA ActiveForm statements from the BioPAX model.
This method extracts ActiveForm Statements that are due to
protein modifications. This method reuses the structure of
BioPAX Pattern's
org.biopax.paxtools.pattern.PatternBox.constrolsStateChange pattern
with additional constraints to specify the gain or loss of a
modification occurring (phosphorylation, deubiquitination, etc.)
and the gain or loss of activity due to the modification state
change. | [
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18,823 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor.get_regulate_amounts | def get_regulate_amounts(self):
"""Extract INDRA RegulateAmount Statements from the BioPAX model.
This method extracts IncreaseAmount/DecreaseAmount Statements from
the BioPAX model. It fully reuses BioPAX Pattern's
org.biopax.paxtools.pattern.PatternBox.controlsExpressionWithTemplateReac
pattern to find TemplateReactions which control the expression of
a protein.
"""
p = pb.controlsExpressionWithTemplateReac()
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
stmts = []
for res in res_array:
# FIXME: for some reason labels are not accessible
# for these queries. It would be more reliable
# to get results by label instead of index.
'''
controller_er = res[p.indexOf('controller ER')]
generic_controller_er = res[p.indexOf('generic controller ER')]
controller_simple_pe = res[p.indexOf('controller simple PE')]
controller_pe = res[p.indexOf('controller PE')]
control = res[p.indexOf('Control')]
conversion = res[p.indexOf('Conversion')]
input_pe = res[p.indexOf('input PE')]
input_simple_pe = res[p.indexOf('input simple PE')]
changed_generic_er = res[p.indexOf('changed generic ER')]
output_pe = res[p.indexOf('output PE')]
output_simple_pe = res[p.indexOf('output simple PE')]
changed_er = res[p.indexOf('changed ER')]
'''
# TODO: here, res[3] is the complex physical entity
# for instance http://pathwaycommons.org/pc2/
# Complex_43c6b8330562c1b411d21e9d1185bae9
# consists of 3 components: JUN, FOS and NFAT
# where NFAT further contains 3 member physical entities.
#
# However, res[2] iterates over all 5 member physical entities
# of the complex which doesn't represent the underlying
# structure faithfully. It would be better to use res[3]
# (the complex itself) and look at components and then
# members. However, then, it would not be clear how to
# construct an INDRA Agent for the controller.
controller = self._get_agents_from_entity(res[2])
controlled_pe = res[6]
controlled = self._get_agents_from_entity(controlled_pe)
conversion = res[5]
direction = conversion.getTemplateDirection()
if direction is not None:
direction = direction.name()
if direction != 'FORWARD':
logger.warning('Unhandled conversion direction %s' %
direction)
continue
# Sometimes interaction type is annotated as
# term=='TRANSCRIPTION'. Other times this is not
# annotated.
int_type = conversion.getInteractionType().toArray()
if int_type:
for it in int_type:
for term in it.getTerm().toArray():
pass
control = res[4]
control_type = control.getControlType()
if control_type:
control_type = control_type.name()
ev = self._get_evidence(control)
for subj, obj in itertools.product(_listify(controller),
_listify(controlled)):
subj_act = ActivityCondition('transcription', True)
subj.activity = subj_act
if control_type == 'ACTIVATION':
st = IncreaseAmount(subj, obj, evidence=ev)
elif control_type == 'INHIBITION':
st = DecreaseAmount(subj, obj, evidence=ev)
else:
logger.warning('Unhandled control type %s' % control_type)
continue
st_dec = decode_obj(st, encoding='utf-8')
self.statements.append(st_dec) | python | def get_regulate_amounts(self):
p = pb.controlsExpressionWithTemplateReac()
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
stmts = []
for res in res_array:
# FIXME: for some reason labels are not accessible
# for these queries. It would be more reliable
# to get results by label instead of index.
'''
controller_er = res[p.indexOf('controller ER')]
generic_controller_er = res[p.indexOf('generic controller ER')]
controller_simple_pe = res[p.indexOf('controller simple PE')]
controller_pe = res[p.indexOf('controller PE')]
control = res[p.indexOf('Control')]
conversion = res[p.indexOf('Conversion')]
input_pe = res[p.indexOf('input PE')]
input_simple_pe = res[p.indexOf('input simple PE')]
changed_generic_er = res[p.indexOf('changed generic ER')]
output_pe = res[p.indexOf('output PE')]
output_simple_pe = res[p.indexOf('output simple PE')]
changed_er = res[p.indexOf('changed ER')]
'''
# TODO: here, res[3] is the complex physical entity
# for instance http://pathwaycommons.org/pc2/
# Complex_43c6b8330562c1b411d21e9d1185bae9
# consists of 3 components: JUN, FOS and NFAT
# where NFAT further contains 3 member physical entities.
#
# However, res[2] iterates over all 5 member physical entities
# of the complex which doesn't represent the underlying
# structure faithfully. It would be better to use res[3]
# (the complex itself) and look at components and then
# members. However, then, it would not be clear how to
# construct an INDRA Agent for the controller.
controller = self._get_agents_from_entity(res[2])
controlled_pe = res[6]
controlled = self._get_agents_from_entity(controlled_pe)
conversion = res[5]
direction = conversion.getTemplateDirection()
if direction is not None:
direction = direction.name()
if direction != 'FORWARD':
logger.warning('Unhandled conversion direction %s' %
direction)
continue
# Sometimes interaction type is annotated as
# term=='TRANSCRIPTION'. Other times this is not
# annotated.
int_type = conversion.getInteractionType().toArray()
if int_type:
for it in int_type:
for term in it.getTerm().toArray():
pass
control = res[4]
control_type = control.getControlType()
if control_type:
control_type = control_type.name()
ev = self._get_evidence(control)
for subj, obj in itertools.product(_listify(controller),
_listify(controlled)):
subj_act = ActivityCondition('transcription', True)
subj.activity = subj_act
if control_type == 'ACTIVATION':
st = IncreaseAmount(subj, obj, evidence=ev)
elif control_type == 'INHIBITION':
st = DecreaseAmount(subj, obj, evidence=ev)
else:
logger.warning('Unhandled control type %s' % control_type)
continue
st_dec = decode_obj(st, encoding='utf-8')
self.statements.append(st_dec) | [
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This method extracts IncreaseAmount/DecreaseAmount Statements from
the BioPAX model. It fully reuses BioPAX Pattern's
org.biopax.paxtools.pattern.PatternBox.controlsExpressionWithTemplateReac
pattern to find TemplateReactions which control the expression of
a protein. | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/sources/biopax/processor.py#L261-L342 |
18,824 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor.get_gef | def get_gef(self):
"""Extract Gef INDRA Statements from the BioPAX model.
This method uses a custom BioPAX Pattern
(one that is not implemented PatternBox) to query for controlled
BiochemicalReactions in which the same protein is in complex with
GDP on the left hand side and in complex with GTP on the
right hand side. This implies that the controller is a GEF for the
GDP/GTP-bound protein.
"""
p = self._gef_gap_base()
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
for r in res_array:
controller_pe = r[p.indexOf('controller PE')]
input_pe = r[p.indexOf('input PE')]
input_spe = r[p.indexOf('input simple PE')]
output_pe = r[p.indexOf('output PE')]
output_spe = r[p.indexOf('output simple PE')]
reaction = r[p.indexOf('Conversion')]
control = r[p.indexOf('Control')]
# Make sure the GEF is not a complex
# TODO: it could be possible to extract certain complexes here, for
# instance ones that only have a single protein
if _is_complex(controller_pe):
continue
members_in = self._get_complex_members(input_pe)
members_out = self._get_complex_members(output_pe)
if not (members_in and members_out):
continue
# Make sure the outgoing complex has exactly 2 members
# TODO: by finding matching proteins on either side, in principle
# it would be possible to find Gef relationships in complexes
# with more members
if len(members_out) != 2:
continue
# Make sure complex starts with GDP that becomes GTP
gdp_in = False
for member in members_in:
if isinstance(member, Agent) and member.name == 'GDP':
gdp_in = True
gtp_out = False
for member in members_out:
if isinstance(member, Agent) and member.name == 'GTP':
gtp_out = True
if not (gdp_in and gtp_out):
continue
ras_list = self._get_agents_from_entity(input_spe)
gef_list = self._get_agents_from_entity(controller_pe)
ev = self._get_evidence(control)
for gef, ras in itertools.product(_listify(gef_list),
_listify(ras_list)):
st = Gef(gef, ras, evidence=ev)
st_dec = decode_obj(st, encoding='utf-8')
self.statements.append(st_dec) | python | def get_gef(self):
p = self._gef_gap_base()
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
for r in res_array:
controller_pe = r[p.indexOf('controller PE')]
input_pe = r[p.indexOf('input PE')]
input_spe = r[p.indexOf('input simple PE')]
output_pe = r[p.indexOf('output PE')]
output_spe = r[p.indexOf('output simple PE')]
reaction = r[p.indexOf('Conversion')]
control = r[p.indexOf('Control')]
# Make sure the GEF is not a complex
# TODO: it could be possible to extract certain complexes here, for
# instance ones that only have a single protein
if _is_complex(controller_pe):
continue
members_in = self._get_complex_members(input_pe)
members_out = self._get_complex_members(output_pe)
if not (members_in and members_out):
continue
# Make sure the outgoing complex has exactly 2 members
# TODO: by finding matching proteins on either side, in principle
# it would be possible to find Gef relationships in complexes
# with more members
if len(members_out) != 2:
continue
# Make sure complex starts with GDP that becomes GTP
gdp_in = False
for member in members_in:
if isinstance(member, Agent) and member.name == 'GDP':
gdp_in = True
gtp_out = False
for member in members_out:
if isinstance(member, Agent) and member.name == 'GTP':
gtp_out = True
if not (gdp_in and gtp_out):
continue
ras_list = self._get_agents_from_entity(input_spe)
gef_list = self._get_agents_from_entity(controller_pe)
ev = self._get_evidence(control)
for gef, ras in itertools.product(_listify(gef_list),
_listify(ras_list)):
st = Gef(gef, ras, evidence=ev)
st_dec = decode_obj(st, encoding='utf-8')
self.statements.append(st_dec) | [
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This method uses a custom BioPAX Pattern
(one that is not implemented PatternBox) to query for controlled
BiochemicalReactions in which the same protein is in complex with
GDP on the left hand side and in complex with GTP on the
right hand side. This implies that the controller is a GEF for the
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18,825 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor.get_gap | def get_gap(self):
"""Extract Gap INDRA Statements from the BioPAX model.
This method uses a custom BioPAX Pattern
(one that is not implemented PatternBox) to query for controlled
BiochemicalReactions in which the same protein is in complex with
GTP on the left hand side and in complex with GDP on the
right hand side. This implies that the controller is a GAP for the
GDP/GTP-bound protein.
"""
p = self._gef_gap_base()
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
for r in res_array:
controller_pe = r[p.indexOf('controller PE')]
input_pe = r[p.indexOf('input PE')]
input_spe = r[p.indexOf('input simple PE')]
output_pe = r[p.indexOf('output PE')]
output_spe = r[p.indexOf('output simple PE')]
reaction = r[p.indexOf('Conversion')]
control = r[p.indexOf('Control')]
# Make sure the GAP is not a complex
# TODO: it could be possible to extract certain complexes here, for
# instance ones that only have a single protein
if _is_complex(controller_pe):
continue
members_in = self._get_complex_members(input_pe)
members_out = self._get_complex_members(output_pe)
if not (members_in and members_out):
continue
# Make sure the outgoing complex has exactly 2 members
# TODO: by finding matching proteins on either side, in principle
# it would be possible to find Gap relationships in complexes
# with more members
if len(members_out) != 2:
continue
# Make sure complex starts with GDP that becomes GTP
gtp_in = False
for member in members_in:
if isinstance(member, Agent) and member.name == 'GTP':
gtp_in = True
gdp_out = False
for member in members_out:
if isinstance(member, Agent) and member.name == 'GDP':
gdp_out = True
if not (gtp_in and gdp_out):
continue
ras_list = self._get_agents_from_entity(input_spe)
gap_list = self._get_agents_from_entity(controller_pe)
ev = self._get_evidence(control)
for gap, ras in itertools.product(_listify(gap_list),
_listify(ras_list)):
st = Gap(gap, ras, evidence=ev)
st_dec = decode_obj(st, encoding='utf-8')
self.statements.append(st_dec) | python | def get_gap(self):
p = self._gef_gap_base()
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
for r in res_array:
controller_pe = r[p.indexOf('controller PE')]
input_pe = r[p.indexOf('input PE')]
input_spe = r[p.indexOf('input simple PE')]
output_pe = r[p.indexOf('output PE')]
output_spe = r[p.indexOf('output simple PE')]
reaction = r[p.indexOf('Conversion')]
control = r[p.indexOf('Control')]
# Make sure the GAP is not a complex
# TODO: it could be possible to extract certain complexes here, for
# instance ones that only have a single protein
if _is_complex(controller_pe):
continue
members_in = self._get_complex_members(input_pe)
members_out = self._get_complex_members(output_pe)
if not (members_in and members_out):
continue
# Make sure the outgoing complex has exactly 2 members
# TODO: by finding matching proteins on either side, in principle
# it would be possible to find Gap relationships in complexes
# with more members
if len(members_out) != 2:
continue
# Make sure complex starts with GDP that becomes GTP
gtp_in = False
for member in members_in:
if isinstance(member, Agent) and member.name == 'GTP':
gtp_in = True
gdp_out = False
for member in members_out:
if isinstance(member, Agent) and member.name == 'GDP':
gdp_out = True
if not (gtp_in and gdp_out):
continue
ras_list = self._get_agents_from_entity(input_spe)
gap_list = self._get_agents_from_entity(controller_pe)
ev = self._get_evidence(control)
for gap, ras in itertools.product(_listify(gap_list),
_listify(ras_list)):
st = Gap(gap, ras, evidence=ev)
st_dec = decode_obj(st, encoding='utf-8')
self.statements.append(st_dec) | [
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BiochemicalReactions in which the same protein is in complex with
GTP on the left hand side and in complex with GDP on the
right hand side. This implies that the controller is a GAP for the
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18,826 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor._get_entity_mods | def _get_entity_mods(bpe):
"""Get all the modifications of an entity in INDRA format"""
if _is_entity(bpe):
features = bpe.getFeature().toArray()
else:
features = bpe.getEntityFeature().toArray()
mods = []
for feature in features:
if not _is_modification(feature):
continue
mc = BiopaxProcessor._extract_mod_from_feature(feature)
if mc is not None:
mods.append(mc)
return mods | python | def _get_entity_mods(bpe):
if _is_entity(bpe):
features = bpe.getFeature().toArray()
else:
features = bpe.getEntityFeature().toArray()
mods = []
for feature in features:
if not _is_modification(feature):
continue
mc = BiopaxProcessor._extract_mod_from_feature(feature)
if mc is not None:
mods.append(mc)
return mods | [
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18,827 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor._get_generic_modification | def _get_generic_modification(self, mod_class):
"""Get all modification reactions given a Modification class."""
mod_type = modclass_to_modtype[mod_class]
if issubclass(mod_class, RemoveModification):
mod_gain_const = mcct.LOSS
mod_type = modtype_to_inverse[mod_type]
else:
mod_gain_const = mcct.GAIN
mod_filter = mod_type[:5]
# Start with a generic modification pattern
p = BiopaxProcessor._construct_modification_pattern()
p.add(mcc(mod_gain_const, mod_filter),
"input simple PE", "output simple PE")
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
stmts = []
for r in res_array:
controller_pe = r[p.indexOf('controller PE')]
input_pe = r[p.indexOf('input PE')]
input_spe = r[p.indexOf('input simple PE')]
output_spe = r[p.indexOf('output simple PE')]
reaction = r[p.indexOf('Conversion')]
control = r[p.indexOf('Control')]
if not _is_catalysis(control):
continue
cat_dir = control.getCatalysisDirection()
if cat_dir is not None and cat_dir.name() != 'LEFT_TO_RIGHT':
logger.debug('Unexpected catalysis direction: %s.' % \
control.getCatalysisDirection())
continue
enzs = BiopaxProcessor._get_primary_controller(controller_pe)
if not enzs:
continue
'''
if _is_complex(input_pe):
sub_members_in = self._get_complex_members(input_pe)
sub_members_out = self._get_complex_members(output_pe)
# TODO: It is possible to find which member of the complex is
# actually modified. That member will be the substrate and
# all other members of the complex will be bound to it.
logger.info('Cannot handle complex substrates.')
continue
'''
subs = BiopaxProcessor._get_agents_from_entity(input_spe,
expand_pe=False)
ev = self._get_evidence(control)
for enz, sub in itertools.product(_listify(enzs), _listify(subs)):
# Get the modifications
mod_in = \
BiopaxProcessor._get_entity_mods(input_spe)
mod_out = \
BiopaxProcessor._get_entity_mods(output_spe)
sub.mods = _get_mod_intersection(mod_in, mod_out)
if issubclass(mod_class, AddModification):
gained_mods = _get_mod_difference(mod_out, mod_in)
else:
gained_mods = _get_mod_difference(mod_in, mod_out)
for mod in gained_mods:
# Is it guaranteed that these are all modifications
# of the type we are extracting?
if mod.mod_type not in (mod_type,
modtype_to_inverse[mod_type]):
continue
stmt = mod_class(enz, sub, mod.residue, mod.position,
evidence=ev)
stmts.append(decode_obj(stmt, encoding='utf-8'))
return stmts | python | def _get_generic_modification(self, mod_class):
mod_type = modclass_to_modtype[mod_class]
if issubclass(mod_class, RemoveModification):
mod_gain_const = mcct.LOSS
mod_type = modtype_to_inverse[mod_type]
else:
mod_gain_const = mcct.GAIN
mod_filter = mod_type[:5]
# Start with a generic modification pattern
p = BiopaxProcessor._construct_modification_pattern()
p.add(mcc(mod_gain_const, mod_filter),
"input simple PE", "output simple PE")
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
stmts = []
for r in res_array:
controller_pe = r[p.indexOf('controller PE')]
input_pe = r[p.indexOf('input PE')]
input_spe = r[p.indexOf('input simple PE')]
output_spe = r[p.indexOf('output simple PE')]
reaction = r[p.indexOf('Conversion')]
control = r[p.indexOf('Control')]
if not _is_catalysis(control):
continue
cat_dir = control.getCatalysisDirection()
if cat_dir is not None and cat_dir.name() != 'LEFT_TO_RIGHT':
logger.debug('Unexpected catalysis direction: %s.' % \
control.getCatalysisDirection())
continue
enzs = BiopaxProcessor._get_primary_controller(controller_pe)
if not enzs:
continue
'''
if _is_complex(input_pe):
sub_members_in = self._get_complex_members(input_pe)
sub_members_out = self._get_complex_members(output_pe)
# TODO: It is possible to find which member of the complex is
# actually modified. That member will be the substrate and
# all other members of the complex will be bound to it.
logger.info('Cannot handle complex substrates.')
continue
'''
subs = BiopaxProcessor._get_agents_from_entity(input_spe,
expand_pe=False)
ev = self._get_evidence(control)
for enz, sub in itertools.product(_listify(enzs), _listify(subs)):
# Get the modifications
mod_in = \
BiopaxProcessor._get_entity_mods(input_spe)
mod_out = \
BiopaxProcessor._get_entity_mods(output_spe)
sub.mods = _get_mod_intersection(mod_in, mod_out)
if issubclass(mod_class, AddModification):
gained_mods = _get_mod_difference(mod_out, mod_in)
else:
gained_mods = _get_mod_difference(mod_in, mod_out)
for mod in gained_mods:
# Is it guaranteed that these are all modifications
# of the type we are extracting?
if mod.mod_type not in (mod_type,
modtype_to_inverse[mod_type]):
continue
stmt = mod_class(enz, sub, mod.residue, mod.position,
evidence=ev)
stmts.append(decode_obj(stmt, encoding='utf-8'))
return stmts | [
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18,828 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor._construct_modification_pattern | def _construct_modification_pattern():
"""Construct the BioPAX pattern to extract modification reactions."""
# The following constraints were pieced together based on the
# following two higher level constrains: pb.controlsStateChange(),
# pb.controlsPhosphorylation().
p = _bpp('Pattern')(_bpimpl('PhysicalEntity')().getModelInterface(),
'controller PE')
# Getting the control itself
p.add(cb.peToControl(), "controller PE", "Control")
# Link the control to the conversion that it controls
p.add(cb.controlToConv(), "Control", "Conversion")
# The controller shouldn't be a participant of the conversion
p.add(_bpp('constraint.NOT')(cb.participant()),
"Conversion", "controller PE")
# Get the input participant of the conversion
p.add(pt(rt.INPUT, True), "Control", "Conversion", "input PE")
# Get the specific PhysicalEntity
p.add(cb.linkToSpecific(), "input PE", "input simple PE")
# Link to ER
p.add(cb.peToER(), "input simple PE", "input simple ER")
# Make sure the participant is a protein
p.add(tp(_bpimpl('Protein')().getModelInterface()), "input simple PE")
# Link to the other side of the conversion
p.add(cs(cst.OTHER_SIDE), "input PE", "Conversion", "output PE")
# Make sure the two sides are not the same
p.add(_bpp('constraint.Equality')(False), "input PE", "output PE")
# Get the specific PhysicalEntity
p.add(cb.linkToSpecific(), "output PE", "output simple PE")
# Link to ER
p.add(cb.peToER(), "output simple PE", "output simple ER")
p.add(_bpp('constraint.Equality')(True), "input simple ER",
"output simple ER")
# Make sure the output is a Protein
p.add(tp(_bpimpl('Protein')().getModelInterface()), "output simple PE")
p.add(_bpp('constraint.NOT')(cb.linkToSpecific()),
"input PE", "output simple PE")
p.add(_bpp('constraint.NOT')(cb.linkToSpecific()),
"output PE", "input simple PE")
return p | python | def _construct_modification_pattern():
# The following constraints were pieced together based on the
# following two higher level constrains: pb.controlsStateChange(),
# pb.controlsPhosphorylation().
p = _bpp('Pattern')(_bpimpl('PhysicalEntity')().getModelInterface(),
'controller PE')
# Getting the control itself
p.add(cb.peToControl(), "controller PE", "Control")
# Link the control to the conversion that it controls
p.add(cb.controlToConv(), "Control", "Conversion")
# The controller shouldn't be a participant of the conversion
p.add(_bpp('constraint.NOT')(cb.participant()),
"Conversion", "controller PE")
# Get the input participant of the conversion
p.add(pt(rt.INPUT, True), "Control", "Conversion", "input PE")
# Get the specific PhysicalEntity
p.add(cb.linkToSpecific(), "input PE", "input simple PE")
# Link to ER
p.add(cb.peToER(), "input simple PE", "input simple ER")
# Make sure the participant is a protein
p.add(tp(_bpimpl('Protein')().getModelInterface()), "input simple PE")
# Link to the other side of the conversion
p.add(cs(cst.OTHER_SIDE), "input PE", "Conversion", "output PE")
# Make sure the two sides are not the same
p.add(_bpp('constraint.Equality')(False), "input PE", "output PE")
# Get the specific PhysicalEntity
p.add(cb.linkToSpecific(), "output PE", "output simple PE")
# Link to ER
p.add(cb.peToER(), "output simple PE", "output simple ER")
p.add(_bpp('constraint.Equality')(True), "input simple ER",
"output simple ER")
# Make sure the output is a Protein
p.add(tp(_bpimpl('Protein')().getModelInterface()), "output simple PE")
p.add(_bpp('constraint.NOT')(cb.linkToSpecific()),
"input PE", "output simple PE")
p.add(_bpp('constraint.NOT')(cb.linkToSpecific()),
"output PE", "input simple PE")
return p | [
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18,829 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor._extract_mod_from_feature | def _extract_mod_from_feature(mf):
"""Extract the type of modification and the position from
a ModificationFeature object in the INDRA format."""
# ModificationFeature / SequenceModificationVocabulary
mf_type = mf.getModificationType()
if mf_type is None:
return None
mf_type_terms = mf_type.getTerm().toArray()
known_mf_type = None
for t in mf_type_terms:
if t.startswith('MOD_RES '):
t = t[8:]
mf_type_indra = _mftype_dict.get(t)
if mf_type_indra is not None:
known_mf_type = mf_type_indra
break
if not known_mf_type:
logger.debug('Skipping modification with unknown terms: %s' %
', '.join(mf_type_terms))
return None
mod_type, residue = known_mf_type
# getFeatureLocation returns SequenceLocation, which is the
# generic parent class of SequenceSite and SequenceInterval.
# Here we need to cast to SequenceSite in order to get to
# the sequence position.
mf_pos = mf.getFeatureLocation()
if mf_pos is not None:
# If it is not a SequenceSite we can't handle it
if not mf_pos.modelInterface.getName() == \
'org.biopax.paxtools.model.level3.SequenceSite':
mod_pos = None
else:
mf_site = cast(_bp('SequenceSite'), mf_pos)
mf_pos_status = mf_site.getPositionStatus()
if mf_pos_status is None:
mod_pos = None
elif mf_pos_status and mf_pos_status.toString() != 'EQUAL':
logger.debug('Modification site position is %s' %
mf_pos_status.toString())
else:
mod_pos = mf_site.getSequencePosition()
mod_pos = '%s' % mod_pos
else:
mod_pos = None
mc = ModCondition(mod_type, residue, mod_pos, True)
return mc | python | def _extract_mod_from_feature(mf):
# ModificationFeature / SequenceModificationVocabulary
mf_type = mf.getModificationType()
if mf_type is None:
return None
mf_type_terms = mf_type.getTerm().toArray()
known_mf_type = None
for t in mf_type_terms:
if t.startswith('MOD_RES '):
t = t[8:]
mf_type_indra = _mftype_dict.get(t)
if mf_type_indra is not None:
known_mf_type = mf_type_indra
break
if not known_mf_type:
logger.debug('Skipping modification with unknown terms: %s' %
', '.join(mf_type_terms))
return None
mod_type, residue = known_mf_type
# getFeatureLocation returns SequenceLocation, which is the
# generic parent class of SequenceSite and SequenceInterval.
# Here we need to cast to SequenceSite in order to get to
# the sequence position.
mf_pos = mf.getFeatureLocation()
if mf_pos is not None:
# If it is not a SequenceSite we can't handle it
if not mf_pos.modelInterface.getName() == \
'org.biopax.paxtools.model.level3.SequenceSite':
mod_pos = None
else:
mf_site = cast(_bp('SequenceSite'), mf_pos)
mf_pos_status = mf_site.getPositionStatus()
if mf_pos_status is None:
mod_pos = None
elif mf_pos_status and mf_pos_status.toString() != 'EQUAL':
logger.debug('Modification site position is %s' %
mf_pos_status.toString())
else:
mod_pos = mf_site.getSequencePosition()
mod_pos = '%s' % mod_pos
else:
mod_pos = None
mc = ModCondition(mod_type, residue, mod_pos, True)
return mc | [
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18,830 | sorgerlab/indra | indra/sources/biopax/processor.py | BiopaxProcessor._get_entref | def _get_entref(bpe):
"""Returns the entity reference of an entity if it exists or
return the entity reference that was passed in as argument."""
if not _is_reference(bpe):
try:
er = bpe.getEntityReference()
except AttributeError:
return None
return er
else:
return bpe | python | def _get_entref(bpe):
if not _is_reference(bpe):
try:
er = bpe.getEntityReference()
except AttributeError:
return None
return er
else:
return bpe | [
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18,831 | sorgerlab/indra | indra/sources/trips/processor.py | _stmt_location_to_agents | def _stmt_location_to_agents(stmt, location):
"""Apply an event location to the Agents in the corresponding Statement.
If a Statement is in a given location we represent that by requiring all
Agents in the Statement to be in that location.
"""
if location is None:
return
agents = stmt.agent_list()
for a in agents:
if a is not None:
a.location = location | python | def _stmt_location_to_agents(stmt, location):
if location is None:
return
agents = stmt.agent_list()
for a in agents:
if a is not None:
a.location = location | [
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18,832 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_all_events | def get_all_events(self):
"""Make a list of all events in the TRIPS EKB.
The events are stored in self.all_events.
"""
self.all_events = {}
events = self.tree.findall('EVENT')
events += self.tree.findall('CC')
for e in events:
event_id = e.attrib['id']
if event_id in self._static_events:
continue
event_type = e.find('type').text
try:
self.all_events[event_type].append(event_id)
except KeyError:
self.all_events[event_type] = [event_id] | python | def get_all_events(self):
self.all_events = {}
events = self.tree.findall('EVENT')
events += self.tree.findall('CC')
for e in events:
event_id = e.attrib['id']
if event_id in self._static_events:
continue
event_type = e.find('type').text
try:
self.all_events[event_type].append(event_id)
except KeyError:
self.all_events[event_type] = [event_id] | [
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18,833 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_activations | def get_activations(self):
"""Extract direct Activation INDRA Statements."""
act_events = self.tree.findall("EVENT/[type='ONT::ACTIVATE']")
inact_events = self.tree.findall("EVENT/[type='ONT::DEACTIVATE']")
inact_events += self.tree.findall("EVENT/[type='ONT::INHIBIT']")
for event in (act_events + inact_events):
event_id = event.attrib['id']
if event_id in self._static_events:
continue
# Get the activating agent in the event
agent = event.find(".//*[@role=':AGENT']")
if agent is None:
continue
agent_id = agent.attrib.get('id')
if agent_id is None:
logger.debug(
'Skipping activation with missing activator agent')
continue
activator_agent = self._get_agent_by_id(agent_id, event_id)
if activator_agent is None:
continue
# Get the activated agent in the event
affected = event.find(".//*[@role=':AFFECTED']")
if affected is None:
logger.debug(
'Skipping activation with missing affected agent')
continue
affected_id = affected.attrib.get('id')
if affected_id is None:
logger.debug(
'Skipping activation with missing affected agent')
continue
affected_agent = self._get_agent_by_id(affected_id, event_id)
if affected_agent is None:
logger.debug(
'Skipping activation with missing affected agent')
continue
is_activation = True
if _is_type(event, 'ONT::ACTIVATE'):
self._add_extracted('ONT::ACTIVATE', event.attrib['id'])
elif _is_type(event, 'ONT::INHIBIT'):
is_activation = False
self._add_extracted('ONT::INHIBIT', event.attrib['id'])
elif _is_type(event, 'ONT::DEACTIVATE'):
is_activation = False
self._add_extracted('ONT::DEACTIVATE', event.attrib['id'])
ev = self._get_evidence(event)
location = self._get_event_location(event)
for a1, a2 in _agent_list_product((activator_agent,
affected_agent)):
if is_activation:
st = Activation(a1, a2, evidence=[deepcopy(ev)])
else:
st = Inhibition(a1, a2, evidence=[deepcopy(ev)])
_stmt_location_to_agents(st, location)
self.statements.append(st) | python | def get_activations(self):
act_events = self.tree.findall("EVENT/[type='ONT::ACTIVATE']")
inact_events = self.tree.findall("EVENT/[type='ONT::DEACTIVATE']")
inact_events += self.tree.findall("EVENT/[type='ONT::INHIBIT']")
for event in (act_events + inact_events):
event_id = event.attrib['id']
if event_id in self._static_events:
continue
# Get the activating agent in the event
agent = event.find(".//*[@role=':AGENT']")
if agent is None:
continue
agent_id = agent.attrib.get('id')
if agent_id is None:
logger.debug(
'Skipping activation with missing activator agent')
continue
activator_agent = self._get_agent_by_id(agent_id, event_id)
if activator_agent is None:
continue
# Get the activated agent in the event
affected = event.find(".//*[@role=':AFFECTED']")
if affected is None:
logger.debug(
'Skipping activation with missing affected agent')
continue
affected_id = affected.attrib.get('id')
if affected_id is None:
logger.debug(
'Skipping activation with missing affected agent')
continue
affected_agent = self._get_agent_by_id(affected_id, event_id)
if affected_agent is None:
logger.debug(
'Skipping activation with missing affected agent')
continue
is_activation = True
if _is_type(event, 'ONT::ACTIVATE'):
self._add_extracted('ONT::ACTIVATE', event.attrib['id'])
elif _is_type(event, 'ONT::INHIBIT'):
is_activation = False
self._add_extracted('ONT::INHIBIT', event.attrib['id'])
elif _is_type(event, 'ONT::DEACTIVATE'):
is_activation = False
self._add_extracted('ONT::DEACTIVATE', event.attrib['id'])
ev = self._get_evidence(event)
location = self._get_event_location(event)
for a1, a2 in _agent_list_product((activator_agent,
affected_agent)):
if is_activation:
st = Activation(a1, a2, evidence=[deepcopy(ev)])
else:
st = Inhibition(a1, a2, evidence=[deepcopy(ev)])
_stmt_location_to_agents(st, location)
self.statements.append(st) | [
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"\"EVENT/[type='ONT::DEACTIVATE']\"",
")",
"inact_... | Extract direct Activation INDRA Statements. | [
"Extract",
"direct",
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"INDRA",
"Statements",
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/sources/trips/processor.py#L116-L174 |
18,834 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_activations_causal | def get_activations_causal(self):
"""Extract causal Activation INDRA Statements."""
# Search for causal connectives of type ONT::CAUSE
ccs = self.tree.findall("CC/[type='ONT::CAUSE']")
for cc in ccs:
factor = cc.find("arg/[@role=':FACTOR']")
outcome = cc.find("arg/[@role=':OUTCOME']")
# If either the factor or the outcome is missing, skip
if factor is None or outcome is None:
continue
factor_id = factor.attrib.get('id')
# Here, implicitly, we require that the factor is a TERM
# and not an EVENT
factor_term = self.tree.find("TERM/[@id='%s']" % factor_id)
outcome_id = outcome.attrib.get('id')
# Here it is implicit that the outcome is an event not
# a TERM
outcome_event = self.tree.find("EVENT/[@id='%s']" % outcome_id)
if factor_term is None or outcome_event is None:
continue
factor_term_type = factor_term.find('type')
# The factor term must be a molecular entity
if factor_term_type is None or \
factor_term_type.text not in molecule_types:
continue
factor_agent = self._get_agent_by_id(factor_id, None)
if factor_agent is None:
continue
outcome_event_type = outcome_event.find('type')
if outcome_event_type is None:
continue
# Construct evidence
ev = self._get_evidence(cc)
ev.epistemics['direct'] = False
location = self._get_event_location(outcome_event)
if outcome_event_type.text in ['ONT::ACTIVATE', 'ONT::ACTIVITY',
'ONT::DEACTIVATE']:
if outcome_event_type.text in ['ONT::ACTIVATE',
'ONT::DEACTIVATE']:
agent_tag = outcome_event.find(".//*[@role=':AFFECTED']")
elif outcome_event_type.text == 'ONT::ACTIVITY':
agent_tag = outcome_event.find(".//*[@role=':AGENT']")
if agent_tag is None or agent_tag.attrib.get('id') is None:
continue
outcome_agent = self._get_agent_by_id(agent_tag.attrib['id'],
outcome_id)
if outcome_agent is None:
continue
if outcome_event_type.text == 'ONT::DEACTIVATE':
is_activation = False
else:
is_activation = True
for a1, a2 in _agent_list_product((factor_agent,
outcome_agent)):
if is_activation:
st = Activation(a1, a2, evidence=[deepcopy(ev)])
else:
st = Inhibition(a1, a2, evidence=[deepcopy(ev)])
_stmt_location_to_agents(st, location)
self.statements.append(st) | python | def get_activations_causal(self):
# Search for causal connectives of type ONT::CAUSE
ccs = self.tree.findall("CC/[type='ONT::CAUSE']")
for cc in ccs:
factor = cc.find("arg/[@role=':FACTOR']")
outcome = cc.find("arg/[@role=':OUTCOME']")
# If either the factor or the outcome is missing, skip
if factor is None or outcome is None:
continue
factor_id = factor.attrib.get('id')
# Here, implicitly, we require that the factor is a TERM
# and not an EVENT
factor_term = self.tree.find("TERM/[@id='%s']" % factor_id)
outcome_id = outcome.attrib.get('id')
# Here it is implicit that the outcome is an event not
# a TERM
outcome_event = self.tree.find("EVENT/[@id='%s']" % outcome_id)
if factor_term is None or outcome_event is None:
continue
factor_term_type = factor_term.find('type')
# The factor term must be a molecular entity
if factor_term_type is None or \
factor_term_type.text not in molecule_types:
continue
factor_agent = self._get_agent_by_id(factor_id, None)
if factor_agent is None:
continue
outcome_event_type = outcome_event.find('type')
if outcome_event_type is None:
continue
# Construct evidence
ev = self._get_evidence(cc)
ev.epistemics['direct'] = False
location = self._get_event_location(outcome_event)
if outcome_event_type.text in ['ONT::ACTIVATE', 'ONT::ACTIVITY',
'ONT::DEACTIVATE']:
if outcome_event_type.text in ['ONT::ACTIVATE',
'ONT::DEACTIVATE']:
agent_tag = outcome_event.find(".//*[@role=':AFFECTED']")
elif outcome_event_type.text == 'ONT::ACTIVITY':
agent_tag = outcome_event.find(".//*[@role=':AGENT']")
if agent_tag is None or agent_tag.attrib.get('id') is None:
continue
outcome_agent = self._get_agent_by_id(agent_tag.attrib['id'],
outcome_id)
if outcome_agent is None:
continue
if outcome_event_type.text == 'ONT::DEACTIVATE':
is_activation = False
else:
is_activation = True
for a1, a2 in _agent_list_product((factor_agent,
outcome_agent)):
if is_activation:
st = Activation(a1, a2, evidence=[deepcopy(ev)])
else:
st = Inhibition(a1, a2, evidence=[deepcopy(ev)])
_stmt_location_to_agents(st, location)
self.statements.append(st) | [
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18,835 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_activations_stimulate | def get_activations_stimulate(self):
"""Extract Activation INDRA Statements via stimulation."""
# TODO: extract to other patterns:
# - Stimulation by EGF activates ERK
# - Stimulation by EGF leads to ERK activation
# Search for stimulation event
stim_events = self.tree.findall("EVENT/[type='ONT::STIMULATE']")
for event in stim_events:
event_id = event.attrib.get('id')
if event_id in self._static_events:
continue
controller = event.find("arg1/[@role=':AGENT']")
affected = event.find("arg2/[@role=':AFFECTED']")
# If either the controller or the affected is missing, skip
if controller is None or affected is None:
continue
controller_id = controller.attrib.get('id')
# Here, implicitly, we require that the controller is a TERM
# and not an EVENT
controller_term = self.tree.find("TERM/[@id='%s']" % controller_id)
affected_id = affected.attrib.get('id')
# Here it is implicit that the affected is an event not
# a TERM
affected_event = self.tree.find("EVENT/[@id='%s']" % affected_id)
if controller_term is None or affected_event is None:
continue
controller_term_type = controller_term.find('type')
# The controller term must be a molecular entity
if controller_term_type is None or \
controller_term_type.text not in molecule_types:
continue
controller_agent = self._get_agent_by_id(controller_id, None)
if controller_agent is None:
continue
affected_event_type = affected_event.find('type')
if affected_event_type is None:
continue
# Construct evidence
ev = self._get_evidence(event)
ev.epistemics['direct'] = False
location = self._get_event_location(affected_event)
if affected_event_type.text == 'ONT::ACTIVATE':
affected = affected_event.find(".//*[@role=':AFFECTED']")
if affected is None:
continue
affected_agent = self._get_agent_by_id(affected.attrib['id'],
affected_id)
if affected_agent is None:
continue
for a1, a2 in _agent_list_product((controller_agent,
affected_agent)):
st = Activation(a1, a2, evidence=[deepcopy(ev)])
_stmt_location_to_agents(st, location)
self.statements.append(st)
elif affected_event_type.text == 'ONT::ACTIVITY':
agent_tag = affected_event.find(".//*[@role=':AGENT']")
if agent_tag is None:
continue
affected_agent = self._get_agent_by_id(agent_tag.attrib['id'],
affected_id)
if affected_agent is None:
continue
for a1, a2 in _agent_list_product((controller_agent,
affected_agent)):
st = Activation(a1, a2, evidence=[deepcopy(ev)])
_stmt_location_to_agents(st, location)
self.statements.append(st) | python | def get_activations_stimulate(self):
# TODO: extract to other patterns:
# - Stimulation by EGF activates ERK
# - Stimulation by EGF leads to ERK activation
# Search for stimulation event
stim_events = self.tree.findall("EVENT/[type='ONT::STIMULATE']")
for event in stim_events:
event_id = event.attrib.get('id')
if event_id in self._static_events:
continue
controller = event.find("arg1/[@role=':AGENT']")
affected = event.find("arg2/[@role=':AFFECTED']")
# If either the controller or the affected is missing, skip
if controller is None or affected is None:
continue
controller_id = controller.attrib.get('id')
# Here, implicitly, we require that the controller is a TERM
# and not an EVENT
controller_term = self.tree.find("TERM/[@id='%s']" % controller_id)
affected_id = affected.attrib.get('id')
# Here it is implicit that the affected is an event not
# a TERM
affected_event = self.tree.find("EVENT/[@id='%s']" % affected_id)
if controller_term is None or affected_event is None:
continue
controller_term_type = controller_term.find('type')
# The controller term must be a molecular entity
if controller_term_type is None or \
controller_term_type.text not in molecule_types:
continue
controller_agent = self._get_agent_by_id(controller_id, None)
if controller_agent is None:
continue
affected_event_type = affected_event.find('type')
if affected_event_type is None:
continue
# Construct evidence
ev = self._get_evidence(event)
ev.epistemics['direct'] = False
location = self._get_event_location(affected_event)
if affected_event_type.text == 'ONT::ACTIVATE':
affected = affected_event.find(".//*[@role=':AFFECTED']")
if affected is None:
continue
affected_agent = self._get_agent_by_id(affected.attrib['id'],
affected_id)
if affected_agent is None:
continue
for a1, a2 in _agent_list_product((controller_agent,
affected_agent)):
st = Activation(a1, a2, evidence=[deepcopy(ev)])
_stmt_location_to_agents(st, location)
self.statements.append(st)
elif affected_event_type.text == 'ONT::ACTIVITY':
agent_tag = affected_event.find(".//*[@role=':AGENT']")
if agent_tag is None:
continue
affected_agent = self._get_agent_by_id(agent_tag.attrib['id'],
affected_id)
if affected_agent is None:
continue
for a1, a2 in _agent_list_product((controller_agent,
affected_agent)):
st = Activation(a1, a2, evidence=[deepcopy(ev)])
_stmt_location_to_agents(st, location)
self.statements.append(st) | [
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18,836 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_degradations | def get_degradations(self):
"""Extract Degradation INDRA Statements."""
deg_events = self.tree.findall("EVENT/[type='ONT::CONSUME']")
for event in deg_events:
if event.attrib['id'] in self._static_events:
continue
affected = event.find(".//*[@role=':AFFECTED']")
if affected is None:
msg = 'Skipping degradation event with no affected term.'
logger.debug(msg)
continue
# Make sure the degradation is affecting a molecule type
# Temporarily removed for CwC compatibility with no type tag
#affected_type = affected.find('type')
#if affected_type is None or \
# affected_type.text not in molecule_types:
# continue
affected_id = affected.attrib.get('id')
if affected_id is None:
logger.debug(
'Skipping degradation event with missing affected agent')
continue
affected_agent = self._get_agent_by_id(affected_id,
event.attrib['id'])
if affected_agent is None:
logger.debug(
'Skipping degradation event with missing affected agent')
continue
agent = event.find(".//*[@role=':AGENT']")
if agent is None:
agent_agent = None
else:
agent_id = agent.attrib.get('id')
if agent_id is None:
agent_agent = None
else:
agent_agent = self._get_agent_by_id(agent_id,
event.attrib['id'])
ev = self._get_evidence(event)
location = self._get_event_location(event)
for subj, obj in \
_agent_list_product((agent_agent, affected_agent)):
st = DecreaseAmount(subj, obj, evidence=deepcopy(ev))
_stmt_location_to_agents(st, location)
self.statements.append(st)
self._add_extracted(_get_type(event), event.attrib['id']) | python | def get_degradations(self):
deg_events = self.tree.findall("EVENT/[type='ONT::CONSUME']")
for event in deg_events:
if event.attrib['id'] in self._static_events:
continue
affected = event.find(".//*[@role=':AFFECTED']")
if affected is None:
msg = 'Skipping degradation event with no affected term.'
logger.debug(msg)
continue
# Make sure the degradation is affecting a molecule type
# Temporarily removed for CwC compatibility with no type tag
#affected_type = affected.find('type')
#if affected_type is None or \
# affected_type.text not in molecule_types:
# continue
affected_id = affected.attrib.get('id')
if affected_id is None:
logger.debug(
'Skipping degradation event with missing affected agent')
continue
affected_agent = self._get_agent_by_id(affected_id,
event.attrib['id'])
if affected_agent is None:
logger.debug(
'Skipping degradation event with missing affected agent')
continue
agent = event.find(".//*[@role=':AGENT']")
if agent is None:
agent_agent = None
else:
agent_id = agent.attrib.get('id')
if agent_id is None:
agent_agent = None
else:
agent_agent = self._get_agent_by_id(agent_id,
event.attrib['id'])
ev = self._get_evidence(event)
location = self._get_event_location(event)
for subj, obj in \
_agent_list_product((agent_agent, affected_agent)):
st = DecreaseAmount(subj, obj, evidence=deepcopy(ev))
_stmt_location_to_agents(st, location)
self.statements.append(st)
self._add_extracted(_get_type(event), event.attrib['id']) | [
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18,837 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_complexes | def get_complexes(self):
"""Extract Complex INDRA Statements."""
bind_events = self.tree.findall("EVENT/[type='ONT::BIND']")
bind_events += self.tree.findall("EVENT/[type='ONT::INTERACT']")
for event in bind_events:
if event.attrib['id'] in self._static_events:
continue
arg1 = event.find("arg1")
arg2 = event.find("arg2")
# EKB-AGENT
if arg1 is None and arg2 is None:
args = list(event.findall('arg'))
if len(args) < 2:
continue
arg1 = args[0]
arg2 = args[1]
if (arg1 is None or arg1.attrib.get('id') is None) or \
(arg2 is None or arg2.attrib.get('id') is None):
logger.debug('Skipping complex with less than 2 members')
continue
agent1 = self._get_agent_by_id(arg1.attrib['id'],
event.attrib['id'])
agent2 = self._get_agent_by_id(arg2.attrib['id'],
event.attrib['id'])
if agent1 is None or agent2 is None:
logger.debug('Skipping complex with less than 2 members')
continue
# Information on binding site is either attached to the agent term
# in a features/site tag or attached to the event itself in
# a site tag
'''
site_feature = self._find_in_term(arg1.attrib['id'], 'features/site')
if site_feature is not None:
sites, positions = self._get_site_by_id(site_id)
print sites, positions
site_feature = self._find_in_term(arg2.attrib['id'], 'features/site')
if site_feature is not None:
sites, positions = self._get_site_by_id(site_id)
print sites, positions
site = event.find("site")
if site is not None:
sites, positions = self._get_site_by_id(site.attrib['id'])
print sites, positions
'''
ev = self._get_evidence(event)
location = self._get_event_location(event)
for a1, a2 in _agent_list_product((agent1, agent2)):
st = Complex([a1, a2], evidence=deepcopy(ev))
_stmt_location_to_agents(st, location)
self.statements.append(st)
self._add_extracted(_get_type(event), event.attrib['id']) | python | def get_complexes(self):
bind_events = self.tree.findall("EVENT/[type='ONT::BIND']")
bind_events += self.tree.findall("EVENT/[type='ONT::INTERACT']")
for event in bind_events:
if event.attrib['id'] in self._static_events:
continue
arg1 = event.find("arg1")
arg2 = event.find("arg2")
# EKB-AGENT
if arg1 is None and arg2 is None:
args = list(event.findall('arg'))
if len(args) < 2:
continue
arg1 = args[0]
arg2 = args[1]
if (arg1 is None or arg1.attrib.get('id') is None) or \
(arg2 is None or arg2.attrib.get('id') is None):
logger.debug('Skipping complex with less than 2 members')
continue
agent1 = self._get_agent_by_id(arg1.attrib['id'],
event.attrib['id'])
agent2 = self._get_agent_by_id(arg2.attrib['id'],
event.attrib['id'])
if agent1 is None or agent2 is None:
logger.debug('Skipping complex with less than 2 members')
continue
# Information on binding site is either attached to the agent term
# in a features/site tag or attached to the event itself in
# a site tag
'''
site_feature = self._find_in_term(arg1.attrib['id'], 'features/site')
if site_feature is not None:
sites, positions = self._get_site_by_id(site_id)
print sites, positions
site_feature = self._find_in_term(arg2.attrib['id'], 'features/site')
if site_feature is not None:
sites, positions = self._get_site_by_id(site_id)
print sites, positions
site = event.find("site")
if site is not None:
sites, positions = self._get_site_by_id(site.attrib['id'])
print sites, positions
'''
ev = self._get_evidence(event)
location = self._get_event_location(event)
for a1, a2 in _agent_list_product((agent1, agent2)):
st = Complex([a1, a2], evidence=deepcopy(ev))
_stmt_location_to_agents(st, location)
self.statements.append(st)
self._add_extracted(_get_type(event), event.attrib['id']) | [
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18,838 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_modifications | def get_modifications(self):
"""Extract all types of Modification INDRA Statements."""
# Get all the specific mod types
mod_event_types = list(ont_to_mod_type.keys())
# Add ONT::PTMs as a special case
mod_event_types += ['ONT::PTM']
mod_events = []
for mod_event_type in mod_event_types:
events = self.tree.findall("EVENT/[type='%s']" % mod_event_type)
mod_extracted = self.extracted_events.get(mod_event_type, [])
for event in events:
event_id = event.attrib.get('id')
if event_id not in mod_extracted:
mod_events.append(event)
# Iterate over all modification events
for event in mod_events:
stmts = self._get_modification_event(event)
if stmts:
for stmt in stmts:
self.statements.append(stmt) | python | def get_modifications(self):
# Get all the specific mod types
mod_event_types = list(ont_to_mod_type.keys())
# Add ONT::PTMs as a special case
mod_event_types += ['ONT::PTM']
mod_events = []
for mod_event_type in mod_event_types:
events = self.tree.findall("EVENT/[type='%s']" % mod_event_type)
mod_extracted = self.extracted_events.get(mod_event_type, [])
for event in events:
event_id = event.attrib.get('id')
if event_id not in mod_extracted:
mod_events.append(event)
# Iterate over all modification events
for event in mod_events:
stmts = self._get_modification_event(event)
if stmts:
for stmt in stmts:
self.statements.append(stmt) | [
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18,839 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_modifications_indirect | def get_modifications_indirect(self):
"""Extract indirect Modification INDRA Statements."""
# Get all the specific mod types
mod_event_types = list(ont_to_mod_type.keys())
# Add ONT::PTMs as a special case
mod_event_types += ['ONT::PTM']
def get_increase_events(mod_event_types):
mod_events = []
events = self.tree.findall("EVENT/[type='ONT::INCREASE']")
for event in events:
affected = event.find(".//*[@role=':AFFECTED']")
if affected is None:
continue
affected_id = affected.attrib.get('id')
if not affected_id:
continue
pattern = "EVENT/[@id='%s']" % affected_id
affected_event = self.tree.find(pattern)
if affected_event is not None:
affected_type = affected_event.find('type')
if affected_type is not None and \
affected_type.text in mod_event_types:
mod_events.append(event)
return mod_events
def get_cause_events(mod_event_types):
mod_events = []
ccs = self.tree.findall("CC/[type='ONT::CAUSE']")
for cc in ccs:
outcome = cc.find(".//*[@role=':OUTCOME']")
if outcome is None:
continue
outcome_id = outcome.attrib.get('id')
if not outcome_id:
continue
pattern = "EVENT/[@id='%s']" % outcome_id
outcome_event = self.tree.find(pattern)
if outcome_event is not None:
outcome_type = outcome_event.find('type')
if outcome_type is not None and \
outcome_type.text in mod_event_types:
mod_events.append(cc)
return mod_events
mod_events = get_increase_events(mod_event_types)
mod_events += get_cause_events(mod_event_types)
# Iterate over all modification events
for event in mod_events:
event_id = event.attrib['id']
if event_id in self._static_events:
continue
event_type = _get_type(event)
# Get enzyme Agent
enzyme = event.find(".//*[@role=':AGENT']")
if enzyme is None:
enzyme = event.find(".//*[@role=':FACTOR']")
if enzyme is None:
return
enzyme_id = enzyme.attrib.get('id')
if enzyme_id is None:
continue
enzyme_agent = self._get_agent_by_id(enzyme_id, event_id)
affected_event_tag = event.find(".//*[@role=':AFFECTED']")
if affected_event_tag is None:
affected_event_tag = event.find(".//*[@role=':OUTCOME']")
if affected_event_tag is None:
return
affected_id = affected_event_tag.attrib.get('id')
if not affected_id:
return
affected_event = self.tree.find("EVENT/[@id='%s']" % affected_id)
if affected_event is None:
return
# Iterate over all enzyme agents if there are multiple ones
for enz_t in _agent_list_product((enzyme_agent, )):
# enz_t comes out as a tuple so we need to take the first
# element here
enz = enz_t[0]
# Note that we re-run the extraction code here potentially
# multiple times. This is mainly to make sure each Statement
# object created here is independent (i.e. has different UUIDs)
# without having to manipulate it after creation.
stmts = self._get_modification_event(affected_event)
stmts_to_make = []
if stmts:
for stmt in stmts:
# The affected event should have no enzyme but should
# have a substrate
if stmt.enz is None and stmt.sub is not None:
stmts_to_make.append(stmt)
for stmt in stmts_to_make:
stmt.enz = enz
for ev in stmt.evidence:
ev.epistemics['direct'] = False
self.statements.append(stmt)
self._add_extracted(event_type, event.attrib['id'])
self._add_extracted(affected_event.find('type').text, affected_id) | python | def get_modifications_indirect(self):
# Get all the specific mod types
mod_event_types = list(ont_to_mod_type.keys())
# Add ONT::PTMs as a special case
mod_event_types += ['ONT::PTM']
def get_increase_events(mod_event_types):
mod_events = []
events = self.tree.findall("EVENT/[type='ONT::INCREASE']")
for event in events:
affected = event.find(".//*[@role=':AFFECTED']")
if affected is None:
continue
affected_id = affected.attrib.get('id')
if not affected_id:
continue
pattern = "EVENT/[@id='%s']" % affected_id
affected_event = self.tree.find(pattern)
if affected_event is not None:
affected_type = affected_event.find('type')
if affected_type is not None and \
affected_type.text in mod_event_types:
mod_events.append(event)
return mod_events
def get_cause_events(mod_event_types):
mod_events = []
ccs = self.tree.findall("CC/[type='ONT::CAUSE']")
for cc in ccs:
outcome = cc.find(".//*[@role=':OUTCOME']")
if outcome is None:
continue
outcome_id = outcome.attrib.get('id')
if not outcome_id:
continue
pattern = "EVENT/[@id='%s']" % outcome_id
outcome_event = self.tree.find(pattern)
if outcome_event is not None:
outcome_type = outcome_event.find('type')
if outcome_type is not None and \
outcome_type.text in mod_event_types:
mod_events.append(cc)
return mod_events
mod_events = get_increase_events(mod_event_types)
mod_events += get_cause_events(mod_event_types)
# Iterate over all modification events
for event in mod_events:
event_id = event.attrib['id']
if event_id in self._static_events:
continue
event_type = _get_type(event)
# Get enzyme Agent
enzyme = event.find(".//*[@role=':AGENT']")
if enzyme is None:
enzyme = event.find(".//*[@role=':FACTOR']")
if enzyme is None:
return
enzyme_id = enzyme.attrib.get('id')
if enzyme_id is None:
continue
enzyme_agent = self._get_agent_by_id(enzyme_id, event_id)
affected_event_tag = event.find(".//*[@role=':AFFECTED']")
if affected_event_tag is None:
affected_event_tag = event.find(".//*[@role=':OUTCOME']")
if affected_event_tag is None:
return
affected_id = affected_event_tag.attrib.get('id')
if not affected_id:
return
affected_event = self.tree.find("EVENT/[@id='%s']" % affected_id)
if affected_event is None:
return
# Iterate over all enzyme agents if there are multiple ones
for enz_t in _agent_list_product((enzyme_agent, )):
# enz_t comes out as a tuple so we need to take the first
# element here
enz = enz_t[0]
# Note that we re-run the extraction code here potentially
# multiple times. This is mainly to make sure each Statement
# object created here is independent (i.e. has different UUIDs)
# without having to manipulate it after creation.
stmts = self._get_modification_event(affected_event)
stmts_to_make = []
if stmts:
for stmt in stmts:
# The affected event should have no enzyme but should
# have a substrate
if stmt.enz is None and stmt.sub is not None:
stmts_to_make.append(stmt)
for stmt in stmts_to_make:
stmt.enz = enz
for ev in stmt.evidence:
ev.epistemics['direct'] = False
self.statements.append(stmt)
self._add_extracted(event_type, event.attrib['id'])
self._add_extracted(affected_event.find('type').text, affected_id) | [
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18,840 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_agents | def get_agents(self):
"""Return list of INDRA Agents corresponding to TERMs in the EKB.
This is meant to be used when entities e.g. "phosphorylated ERK",
rather than events need to be extracted from processed natural
language. These entities with their respective states are represented
as INDRA Agents.
Returns
-------
agents : list[indra.statements.Agent]
List of INDRA Agents extracted from EKB.
"""
agents_dict = self.get_term_agents()
agents = [a for a in agents_dict.values() if a is not None]
return agents | python | def get_agents(self):
agents_dict = self.get_term_agents()
agents = [a for a in agents_dict.values() if a is not None]
return agents | [
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18,841 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor.get_term_agents | def get_term_agents(self):
"""Return dict of INDRA Agents keyed by corresponding TERMs in the EKB.
This is meant to be used when entities e.g. "phosphorylated ERK",
rather than events need to be extracted from processed natural
language. These entities with their respective states are represented
as INDRA Agents. Further, each key of the dictionary corresponds to
the ID assigned by TRIPS to the given TERM that the Agent was
extracted from.
Returns
-------
agents : dict[str, indra.statements.Agent]
Dict of INDRA Agents extracted from EKB.
"""
terms = self.tree.findall('TERM')
agents = {}
assoc_links = []
for term in terms:
term_id = term.attrib.get('id')
if term_id:
agent = self._get_agent_by_id(term_id, None)
agents[term_id] = agent
# Handle assoc-with links
aw = term.find('assoc-with')
if aw is not None:
aw_id = aw.attrib.get('id')
if aw_id:
assoc_links.append((term_id, aw_id))
# We only keep the target end of assoc with links if both
# source and target are in the list
for source, target in assoc_links:
if target in agents and source in agents:
agents.pop(source)
return agents | python | def get_term_agents(self):
terms = self.tree.findall('TERM')
agents = {}
assoc_links = []
for term in terms:
term_id = term.attrib.get('id')
if term_id:
agent = self._get_agent_by_id(term_id, None)
agents[term_id] = agent
# Handle assoc-with links
aw = term.find('assoc-with')
if aw is not None:
aw_id = aw.attrib.get('id')
if aw_id:
assoc_links.append((term_id, aw_id))
# We only keep the target end of assoc with links if both
# source and target are in the list
for source, target in assoc_links:
if target in agents and source in agents:
agents.pop(source)
return agents | [
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language. These entities with their respective states are represented
as INDRA Agents. Further, each key of the dictionary corresponds to
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18,842 | sorgerlab/indra | indra/sources/trips/processor.py | TripsProcessor._get_evidence_text | def _get_evidence_text(self, event_tag):
"""Extract the evidence for an event.
Pieces of text linked to an EVENT are fragments of a sentence. The
EVENT refers to the paragraph ID and the "uttnum", which corresponds
to a sentence ID. Here we find and return the full sentence from which
the event was taken.
"""
par_id = event_tag.attrib.get('paragraph')
uttnum = event_tag.attrib.get('uttnum')
event_text = event_tag.find('text')
if self.sentences is not None and uttnum is not None:
sentence = self.sentences[uttnum]
elif event_text is not None:
sentence = event_text.text
else:
sentence = None
return sentence | python | def _get_evidence_text(self, event_tag):
par_id = event_tag.attrib.get('paragraph')
uttnum = event_tag.attrib.get('uttnum')
event_text = event_tag.find('text')
if self.sentences is not None and uttnum is not None:
sentence = self.sentences[uttnum]
elif event_text is not None:
sentence = event_text.text
else:
sentence = None
return sentence | [
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18,843 | sorgerlab/indra | indra/assemblers/pybel/assembler.py | get_causal_edge | def get_causal_edge(stmt, activates):
"""Returns the causal, polar edge with the correct "contact"."""
any_contact = any(
evidence.epistemics.get('direct', False)
for evidence in stmt.evidence
)
if any_contact:
return pc.DIRECTLY_INCREASES if activates else pc.DIRECTLY_DECREASES
return pc.INCREASES if activates else pc.DECREASES | python | def get_causal_edge(stmt, activates):
any_contact = any(
evidence.epistemics.get('direct', False)
for evidence in stmt.evidence
)
if any_contact:
return pc.DIRECTLY_INCREASES if activates else pc.DIRECTLY_DECREASES
return pc.INCREASES if activates else pc.DECREASES | [
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18,844 | sorgerlab/indra | indra/assemblers/pybel/assembler.py | PybelAssembler.to_database | def to_database(self, manager=None):
"""Send the model to the PyBEL database
This function wraps :py:func:`pybel.to_database`.
Parameters
----------
manager : Optional[pybel.manager.Manager]
A PyBEL database manager. If none, first checks the PyBEL
configuration for ``PYBEL_CONNECTION`` then checks the
environment variable ``PYBEL_REMOTE_HOST``. Finally,
defaults to using SQLite database in PyBEL data directory
(automatically configured by PyBEL)
Returns
-------
network : Optional[pybel.manager.models.Network]
The SQLAlchemy model representing the network that was uploaded.
Returns None if upload fails.
"""
network = pybel.to_database(self.model, manager=manager)
return network | python | def to_database(self, manager=None):
network = pybel.to_database(self.model, manager=manager)
return network | [
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This function wraps :py:func:`pybel.to_database`.
Parameters
----------
manager : Optional[pybel.manager.Manager]
A PyBEL database manager. If none, first checks the PyBEL
configuration for ``PYBEL_CONNECTION`` then checks the
environment variable ``PYBEL_REMOTE_HOST``. Finally,
defaults to using SQLite database in PyBEL data directory
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Returns
-------
network : Optional[pybel.manager.models.Network]
The SQLAlchemy model representing the network that was uploaded.
Returns None if upload fails. | [
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18,845 | sorgerlab/indra | indra/assemblers/pysb/sites.py | get_binding_site_name | def get_binding_site_name(agent):
"""Return a binding site name from a given agent."""
# Try to construct a binding site name based on parent
grounding = agent.get_grounding()
if grounding != (None, None):
uri = hierarchies['entity'].get_uri(grounding[0], grounding[1])
# Get highest level parents in hierarchy
parents = hierarchies['entity'].get_parents(uri, 'top')
if parents:
# Choose the first parent if there are more than one
parent_uri = sorted(parents)[0]
parent_agent = _agent_from_uri(parent_uri)
binding_site = _n(parent_agent.name).lower()
return binding_site
# Fall back to Agent's own name if one from parent can't be constructed
binding_site = _n(agent.name).lower()
return binding_site | python | def get_binding_site_name(agent):
# Try to construct a binding site name based on parent
grounding = agent.get_grounding()
if grounding != (None, None):
uri = hierarchies['entity'].get_uri(grounding[0], grounding[1])
# Get highest level parents in hierarchy
parents = hierarchies['entity'].get_parents(uri, 'top')
if parents:
# Choose the first parent if there are more than one
parent_uri = sorted(parents)[0]
parent_agent = _agent_from_uri(parent_uri)
binding_site = _n(parent_agent.name).lower()
return binding_site
# Fall back to Agent's own name if one from parent can't be constructed
binding_site = _n(agent.name).lower()
return binding_site | [
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18,846 | sorgerlab/indra | indra/assemblers/pysb/sites.py | get_mod_site_name | def get_mod_site_name(mod_condition):
"""Return site names for a modification."""
if mod_condition.residue is None:
mod_str = abbrevs[mod_condition.mod_type]
else:
mod_str = mod_condition.residue
mod_pos = mod_condition.position if \
mod_condition.position is not None else ''
name = ('%s%s' % (mod_str, mod_pos))
return name | python | def get_mod_site_name(mod_condition):
if mod_condition.residue is None:
mod_str = abbrevs[mod_condition.mod_type]
else:
mod_str = mod_condition.residue
mod_pos = mod_condition.position if \
mod_condition.position is not None else ''
name = ('%s%s' % (mod_str, mod_pos))
return name | [
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18,847 | sorgerlab/indra | indra/sources/hprd/api.py | process_flat_files | def process_flat_files(id_mappings_file, complexes_file=None, ptm_file=None,
ppi_file=None, seq_file=None, motif_window=7):
"""Get INDRA Statements from HPRD data.
Of the arguments, `id_mappings_file` is required, and at least one of
`complexes_file`, `ptm_file`, and `ppi_file` must also be given. If
`ptm_file` is given, `seq_file` must also be given.
Note that many proteins (> 1,600) in the HPRD content are associated with
outdated RefSeq IDs that cannot be mapped to Uniprot IDs. For these, the
Uniprot ID obtained from the HGNC ID (itself obtained from the Entrez ID)
is used. Because the sequence referenced by the Uniprot ID obtained this
way may be different from the (outdated) RefSeq sequence included with the
HPRD content, it is possible that this will lead to invalid site positions
with respect to the Uniprot IDs.
To allow these site positions to be mapped during assembly, the
Modification statements produced by the HprdProcessor include an additional
key in the `annotations` field of their Evidence object. The annotations
field is called 'site_motif' and it maps to a dictionary with three
elements: 'motif', 'respos', and 'off_by_one'. 'motif' gives the peptide
sequence obtained from the RefSeq sequence included with HPRD. 'respos'
indicates the position in the peptide sequence containing the residue.
Note that these positions are ONE-INDEXED (not zero-indexed). Finally, the
'off-by-one' field contains a boolean value indicating whether the correct
position was inferred as being an off-by-one (methionine cleavage) error.
If True, it means that the given residue could not be found in the HPRD
RefSeq sequence at the given position, but a matching residue was found at
position+1, suggesting a sequence numbering based on the methionine-cleaved
sequence. The peptide included in the 'site_motif' dictionary is based on
this updated position.
Parameters
----------
id_mappings_file : str
Path to HPRD_ID_MAPPINGS.txt file.
complexes_file : Optional[str]
Path to PROTEIN_COMPLEXES.txt file.
ptm_file : Optional[str]
Path to POST_TRANSLATIONAL_MODIFICATIONS.txt file.
ppi_file : Optional[str]
Path to BINARY_PROTEIN_PROTEIN_INTERACTIONS.txt file.
seq_file : Optional[str]
Path to PROTEIN_SEQUENCES.txt file.
motif_window : int
Number of flanking amino acids to include on each side of the
PTM target residue in the 'site_motif' annotations field of the
Evidence for Modification Statements. Default is 7.
Returns
-------
HprdProcessor
An HprdProcessor object which contains a list of extracted INDRA
Statements in its statements attribute.
"""
id_df = pd.read_csv(id_mappings_file, delimiter='\t', names=_hprd_id_cols,
dtype='str')
id_df = id_df.set_index('HPRD_ID')
if complexes_file is None and ptm_file is None and ppi_file is None:
raise ValueError('At least one of complexes_file, ptm_file, or '
'ppi_file must be given.')
if ptm_file and not seq_file:
raise ValueError('If ptm_file is given, seq_file must also be given.')
# Load complexes into dataframe
cplx_df = None
if complexes_file:
cplx_df = pd.read_csv(complexes_file, delimiter='\t', names=_cplx_cols,
dtype='str', na_values=['-', 'None'])
# Load ptm data into dataframe
ptm_df = None
seq_dict = None
if ptm_file:
ptm_df = pd.read_csv(ptm_file, delimiter='\t', names=_ptm_cols,
dtype='str', na_values='-')
# Load protein sequences as a dict keyed by RefSeq ID
seq_dict = load_fasta_sequences(seq_file, id_index=2)
# Load the PPI data into dataframe
ppi_df = None
if ppi_file:
ppi_df = pd.read_csv(ppi_file, delimiter='\t', names=_ppi_cols,
dtype='str')
# Create the processor
return HprdProcessor(id_df, cplx_df, ptm_df, ppi_df, seq_dict, motif_window) | python | def process_flat_files(id_mappings_file, complexes_file=None, ptm_file=None,
ppi_file=None, seq_file=None, motif_window=7):
id_df = pd.read_csv(id_mappings_file, delimiter='\t', names=_hprd_id_cols,
dtype='str')
id_df = id_df.set_index('HPRD_ID')
if complexes_file is None and ptm_file is None and ppi_file is None:
raise ValueError('At least one of complexes_file, ptm_file, or '
'ppi_file must be given.')
if ptm_file and not seq_file:
raise ValueError('If ptm_file is given, seq_file must also be given.')
# Load complexes into dataframe
cplx_df = None
if complexes_file:
cplx_df = pd.read_csv(complexes_file, delimiter='\t', names=_cplx_cols,
dtype='str', na_values=['-', 'None'])
# Load ptm data into dataframe
ptm_df = None
seq_dict = None
if ptm_file:
ptm_df = pd.read_csv(ptm_file, delimiter='\t', names=_ptm_cols,
dtype='str', na_values='-')
# Load protein sequences as a dict keyed by RefSeq ID
seq_dict = load_fasta_sequences(seq_file, id_index=2)
# Load the PPI data into dataframe
ppi_df = None
if ppi_file:
ppi_df = pd.read_csv(ppi_file, delimiter='\t', names=_ppi_cols,
dtype='str')
# Create the processor
return HprdProcessor(id_df, cplx_df, ptm_df, ppi_df, seq_dict, motif_window) | [
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Of the arguments, `id_mappings_file` is required, and at least one of
`complexes_file`, `ptm_file`, and `ppi_file` must also be given. If
`ptm_file` is given, `seq_file` must also be given.
Note that many proteins (> 1,600) in the HPRD content are associated with
outdated RefSeq IDs that cannot be mapped to Uniprot IDs. For these, the
Uniprot ID obtained from the HGNC ID (itself obtained from the Entrez ID)
is used. Because the sequence referenced by the Uniprot ID obtained this
way may be different from the (outdated) RefSeq sequence included with the
HPRD content, it is possible that this will lead to invalid site positions
with respect to the Uniprot IDs.
To allow these site positions to be mapped during assembly, the
Modification statements produced by the HprdProcessor include an additional
key in the `annotations` field of their Evidence object. The annotations
field is called 'site_motif' and it maps to a dictionary with three
elements: 'motif', 'respos', and 'off_by_one'. 'motif' gives the peptide
sequence obtained from the RefSeq sequence included with HPRD. 'respos'
indicates the position in the peptide sequence containing the residue.
Note that these positions are ONE-INDEXED (not zero-indexed). Finally, the
'off-by-one' field contains a boolean value indicating whether the correct
position was inferred as being an off-by-one (methionine cleavage) error.
If True, it means that the given residue could not be found in the HPRD
RefSeq sequence at the given position, but a matching residue was found at
position+1, suggesting a sequence numbering based on the methionine-cleaved
sequence. The peptide included in the 'site_motif' dictionary is based on
this updated position.
Parameters
----------
id_mappings_file : str
Path to HPRD_ID_MAPPINGS.txt file.
complexes_file : Optional[str]
Path to PROTEIN_COMPLEXES.txt file.
ptm_file : Optional[str]
Path to POST_TRANSLATIONAL_MODIFICATIONS.txt file.
ppi_file : Optional[str]
Path to BINARY_PROTEIN_PROTEIN_INTERACTIONS.txt file.
seq_file : Optional[str]
Path to PROTEIN_SEQUENCES.txt file.
motif_window : int
Number of flanking amino acids to include on each side of the
PTM target residue in the 'site_motif' annotations field of the
Evidence for Modification Statements. Default is 7.
Returns
-------
HprdProcessor
An HprdProcessor object which contains a list of extracted INDRA
Statements in its statements attribute. | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/sources/hprd/api.py#L22-L104 |
18,848 | sorgerlab/indra | indra/assemblers/pysb/preassembler.py | PysbPreassembler._gather_active_forms | def _gather_active_forms(self):
"""Collect all the active forms of each Agent in the Statements."""
for stmt in self.statements:
if isinstance(stmt, ActiveForm):
base_agent = self.agent_set.get_create_base_agent(stmt.agent)
# Handle the case where an activity flag is set
agent_to_add = stmt.agent
if stmt.agent.activity:
new_agent = fast_deepcopy(stmt.agent)
new_agent.activity = None
agent_to_add = new_agent
base_agent.add_activity_form(agent_to_add, stmt.is_active) | python | def _gather_active_forms(self):
for stmt in self.statements:
if isinstance(stmt, ActiveForm):
base_agent = self.agent_set.get_create_base_agent(stmt.agent)
# Handle the case where an activity flag is set
agent_to_add = stmt.agent
if stmt.agent.activity:
new_agent = fast_deepcopy(stmt.agent)
new_agent.activity = None
agent_to_add = new_agent
base_agent.add_activity_form(agent_to_add, stmt.is_active) | [
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18,849 | sorgerlab/indra | indra/assemblers/pysb/preassembler.py | PysbPreassembler.replace_activities | def replace_activities(self):
"""Replace ative flags with Agent states when possible."""
logger.debug('Running PySB Preassembler replace activities')
# TODO: handle activity hierarchies
new_stmts = []
def has_agent_activity(stmt):
"""Return True if any agents in the Statement have activity."""
for agent in stmt.agent_list():
if isinstance(agent, Agent) and agent.activity is not None:
return True
return False
# First collect all explicit active forms
self._gather_active_forms()
# Iterate over all statements
for j, stmt in enumerate(self.statements):
logger.debug('%d/%d %s' % (j + 1, len(self.statements), stmt))
# If the Statement doesn't have any activities, we can just
# keep it and move on
if not has_agent_activity(stmt):
new_stmts.append(stmt)
continue
stmt_agents = stmt.agent_list()
num_agents = len(stmt_agents)
# Make a list with an empty list for each Agent so that later
# we can build combinations of Agent forms
agent_forms = [[] for a in stmt_agents]
for i, agent in enumerate(stmt_agents):
# This is the case where there is an activity flag on an
# Agent which we will attempt to replace with an explicit
# active form
if agent is not None and isinstance(agent, Agent) and \
agent.activity is not None:
base_agent = self.agent_set.get_create_base_agent(agent)
# If it is an "active" state
if agent.activity.is_active:
active_forms = base_agent.active_forms
# If no explicit active forms are known then we use
# the generic one
if not active_forms:
active_forms = [agent]
# If it is an "inactive" state
else:
active_forms = base_agent.inactive_forms
# If no explicit inactive forms are known then we use
# the generic one
if not active_forms:
active_forms = [agent]
# We now iterate over the active agent forms and create
# new agents
for af in active_forms:
new_agent = fast_deepcopy(agent)
self._set_agent_context(af, new_agent)
agent_forms[i].append(new_agent)
# Otherwise we just copy over the agent as is
else:
agent_forms[i].append(agent)
# Now create all possible combinations of the agents and create new
# statements as needed
agent_combs = itertools.product(*agent_forms)
for agent_comb in agent_combs:
new_stmt = fast_deepcopy(stmt)
new_stmt.set_agent_list(agent_comb)
new_stmts.append(new_stmt)
self.statements = new_stmts | python | def replace_activities(self):
logger.debug('Running PySB Preassembler replace activities')
# TODO: handle activity hierarchies
new_stmts = []
def has_agent_activity(stmt):
"""Return True if any agents in the Statement have activity."""
for agent in stmt.agent_list():
if isinstance(agent, Agent) and agent.activity is not None:
return True
return False
# First collect all explicit active forms
self._gather_active_forms()
# Iterate over all statements
for j, stmt in enumerate(self.statements):
logger.debug('%d/%d %s' % (j + 1, len(self.statements), stmt))
# If the Statement doesn't have any activities, we can just
# keep it and move on
if not has_agent_activity(stmt):
new_stmts.append(stmt)
continue
stmt_agents = stmt.agent_list()
num_agents = len(stmt_agents)
# Make a list with an empty list for each Agent so that later
# we can build combinations of Agent forms
agent_forms = [[] for a in stmt_agents]
for i, agent in enumerate(stmt_agents):
# This is the case where there is an activity flag on an
# Agent which we will attempt to replace with an explicit
# active form
if agent is not None and isinstance(agent, Agent) and \
agent.activity is not None:
base_agent = self.agent_set.get_create_base_agent(agent)
# If it is an "active" state
if agent.activity.is_active:
active_forms = base_agent.active_forms
# If no explicit active forms are known then we use
# the generic one
if not active_forms:
active_forms = [agent]
# If it is an "inactive" state
else:
active_forms = base_agent.inactive_forms
# If no explicit inactive forms are known then we use
# the generic one
if not active_forms:
active_forms = [agent]
# We now iterate over the active agent forms and create
# new agents
for af in active_forms:
new_agent = fast_deepcopy(agent)
self._set_agent_context(af, new_agent)
agent_forms[i].append(new_agent)
# Otherwise we just copy over the agent as is
else:
agent_forms[i].append(agent)
# Now create all possible combinations of the agents and create new
# statements as needed
agent_combs = itertools.product(*agent_forms)
for agent_comb in agent_combs:
new_stmt = fast_deepcopy(stmt)
new_stmt.set_agent_list(agent_comb)
new_stmts.append(new_stmt)
self.statements = new_stmts | [
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18,850 | sorgerlab/indra | indra/assemblers/pysb/preassembler.py | PysbPreassembler.add_reverse_effects | def add_reverse_effects(self):
"""Add Statements for the reverse effects of some Statements.
For instance, if a protein is phosphorylated but never dephosphorylated
in the model, we add a generic dephosphorylation here. This step is
usually optional in the assembly process.
"""
# TODO: generalize to other modification sites
pos_mod_sites = {}
neg_mod_sites = {}
syntheses = []
degradations = []
for stmt in self.statements:
if isinstance(stmt, Phosphorylation):
agent = stmt.sub.name
try:
pos_mod_sites[agent].append((stmt.residue, stmt.position))
except KeyError:
pos_mod_sites[agent] = [(stmt.residue, stmt.position)]
elif isinstance(stmt, Dephosphorylation):
agent = stmt.sub.name
try:
neg_mod_sites[agent].append((stmt.residue, stmt.position))
except KeyError:
neg_mod_sites[agent] = [(stmt.residue, stmt.position)]
elif isinstance(stmt, Influence):
if stmt.overall_polarity() == 1:
syntheses.append(stmt.obj.name)
elif stmt.overall_polarity() == -1:
degradations.append(stmt.obj.name)
elif isinstance(stmt, IncreaseAmount):
syntheses.append(stmt.obj.name)
elif isinstance(stmt, DecreaseAmount):
degradations.append(stmt.obj.name)
new_stmts = []
for agent_name, pos_sites in pos_mod_sites.items():
neg_sites = neg_mod_sites.get(agent_name, [])
no_neg_site = set(pos_sites).difference(set(neg_sites))
for residue, position in no_neg_site:
st = Dephosphorylation(Agent('phosphatase'),
Agent(agent_name),
residue, position)
new_stmts.append(st)
for agent_name in syntheses:
if agent_name not in degradations:
st = DecreaseAmount(None, Agent(agent_name))
new_stmts.append(st)
self.statements += new_stmts | python | def add_reverse_effects(self):
# TODO: generalize to other modification sites
pos_mod_sites = {}
neg_mod_sites = {}
syntheses = []
degradations = []
for stmt in self.statements:
if isinstance(stmt, Phosphorylation):
agent = stmt.sub.name
try:
pos_mod_sites[agent].append((stmt.residue, stmt.position))
except KeyError:
pos_mod_sites[agent] = [(stmt.residue, stmt.position)]
elif isinstance(stmt, Dephosphorylation):
agent = stmt.sub.name
try:
neg_mod_sites[agent].append((stmt.residue, stmt.position))
except KeyError:
neg_mod_sites[agent] = [(stmt.residue, stmt.position)]
elif isinstance(stmt, Influence):
if stmt.overall_polarity() == 1:
syntheses.append(stmt.obj.name)
elif stmt.overall_polarity() == -1:
degradations.append(stmt.obj.name)
elif isinstance(stmt, IncreaseAmount):
syntheses.append(stmt.obj.name)
elif isinstance(stmt, DecreaseAmount):
degradations.append(stmt.obj.name)
new_stmts = []
for agent_name, pos_sites in pos_mod_sites.items():
neg_sites = neg_mod_sites.get(agent_name, [])
no_neg_site = set(pos_sites).difference(set(neg_sites))
for residue, position in no_neg_site:
st = Dephosphorylation(Agent('phosphatase'),
Agent(agent_name),
residue, position)
new_stmts.append(st)
for agent_name in syntheses:
if agent_name not in degradations:
st = DecreaseAmount(None, Agent(agent_name))
new_stmts.append(st)
self.statements += new_stmts | [
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For instance, if a protein is phosphorylated but never dephosphorylated
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usually optional in the assembly process. | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/assemblers/pysb/preassembler.py#L107-L156 |
18,851 | sorgerlab/indra | indra/preassembler/sitemapper.py | _get_uniprot_id | def _get_uniprot_id(agent):
"""Return the UniProt ID for an agent, looking up in HGNC if necessary.
If the UniProt ID is a list then return the first ID by default.
"""
up_id = agent.db_refs.get('UP')
hgnc_id = agent.db_refs.get('HGNC')
if up_id is None:
if hgnc_id is None:
# If both UniProt and HGNC refs are missing we can't
# sequence check and so don't report a failure.
return None
# Try to get UniProt ID from HGNC
up_id = hgnc_client.get_uniprot_id(hgnc_id)
# If this fails, again, we can't sequence check
if up_id is None:
return None
# If the UniProt ID is a list then choose the first one.
if not isinstance(up_id, basestring) and \
isinstance(up_id[0], basestring):
up_id = up_id[0]
return up_id | python | def _get_uniprot_id(agent):
up_id = agent.db_refs.get('UP')
hgnc_id = agent.db_refs.get('HGNC')
if up_id is None:
if hgnc_id is None:
# If both UniProt and HGNC refs are missing we can't
# sequence check and so don't report a failure.
return None
# Try to get UniProt ID from HGNC
up_id = hgnc_client.get_uniprot_id(hgnc_id)
# If this fails, again, we can't sequence check
if up_id is None:
return None
# If the UniProt ID is a list then choose the first one.
if not isinstance(up_id, basestring) and \
isinstance(up_id[0], basestring):
up_id = up_id[0]
return up_id | [
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18,852 | sorgerlab/indra | indra/preassembler/sitemapper.py | SiteMapper.map_sites | def map_sites(self, stmts):
"""Check a set of statements for invalid modification sites.
Statements are checked against Uniprot reference sequences to determine
if residues referred to by post-translational modifications exist at
the given positions.
If there is nothing amiss with a statement (modifications on any of the
agents, modifications made in the statement, etc.), then the statement
goes into the list of valid statements. If there is a problem with the
statement, the offending modifications are looked up in the site map
(:py:attr:`site_map`), and an instance of :py:class:`MappedStatement`
is added to the list of mapped statements.
Parameters
----------
stmts : list of :py:class:`indra.statement.Statement`
The statements to check for site errors.
Returns
-------
tuple
2-tuple containing (valid_statements, mapped_statements). The first
element of the tuple is a list of valid statements
(:py:class:`indra.statement.Statement`) that were not found to
contain any site errors. The second element of the tuple is a list
of mapped statements (:py:class:`MappedStatement`) with information
on the incorrect sites and corresponding statements with correctly
mapped sites.
"""
valid_statements = []
mapped_statements = []
for stmt in stmts:
mapped_stmt = self.map_stmt_sites(stmt)
# If we got a MappedStatement as a return value, we add that to the
# list of mapped statements, otherwise, the original Statement is
# not invalid so we add it to the other list directly.
if mapped_stmt is not None:
mapped_statements.append(mapped_stmt)
else:
valid_statements.append(stmt)
return valid_statements, mapped_statements | python | def map_sites(self, stmts):
valid_statements = []
mapped_statements = []
for stmt in stmts:
mapped_stmt = self.map_stmt_sites(stmt)
# If we got a MappedStatement as a return value, we add that to the
# list of mapped statements, otherwise, the original Statement is
# not invalid so we add it to the other list directly.
if mapped_stmt is not None:
mapped_statements.append(mapped_stmt)
else:
valid_statements.append(stmt)
return valid_statements, mapped_statements | [
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If there is nothing amiss with a statement (modifications on any of the
agents, modifications made in the statement, etc.), then the statement
goes into the list of valid statements. If there is a problem with the
statement, the offending modifications are looked up in the site map
(:py:attr:`site_map`), and an instance of :py:class:`MappedStatement`
is added to the list of mapped statements.
Parameters
----------
stmts : list of :py:class:`indra.statement.Statement`
The statements to check for site errors.
Returns
-------
tuple
2-tuple containing (valid_statements, mapped_statements). The first
element of the tuple is a list of valid statements
(:py:class:`indra.statement.Statement`) that were not found to
contain any site errors. The second element of the tuple is a list
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18,853 | sorgerlab/indra | indra/preassembler/sitemapper.py | SiteMapper._map_agent_sites | def _map_agent_sites(self, agent):
"""Check an agent for invalid sites and update if necessary.
Parameters
----------
agent : :py:class:`indra.statements.Agent`
Agent to check for invalid modification sites.
Returns
-------
tuple
The first element is a list of MappedSite objects, the second
element is either the original Agent, if unchanged, or a copy
of it.
"""
# If there are no modifications on this agent, then we can return the
# copy of the agent
if agent is None or not agent.mods:
return [], agent
new_agent = deepcopy(agent)
mapped_sites = []
# Now iterate over all the modifications and map each one
for idx, mod_condition in enumerate(agent.mods):
mapped_site = \
self._map_agent_mod(agent, mod_condition)
# If we couldn't do the mapping or the mapped site isn't invalid
# then we don't need to change the existing ModCondition
if not mapped_site or mapped_site.not_invalid():
continue
# Otherwise, if there is a mapping, we replace the old ModCondition
# with the new one where only the residue and position are updated,
# the mod type and the is modified flag are kept.
if mapped_site.has_mapping():
mc = ModCondition(mod_condition.mod_type,
mapped_site.mapped_res,
mapped_site.mapped_pos,
mod_condition.is_modified)
new_agent.mods[idx] = mc
# Finally, whether or not we have a mapping, we keep track of mapped
# sites and make them available to the caller
mapped_sites.append(mapped_site)
return mapped_sites, new_agent | python | def _map_agent_sites(self, agent):
# If there are no modifications on this agent, then we can return the
# copy of the agent
if agent is None or not agent.mods:
return [], agent
new_agent = deepcopy(agent)
mapped_sites = []
# Now iterate over all the modifications and map each one
for idx, mod_condition in enumerate(agent.mods):
mapped_site = \
self._map_agent_mod(agent, mod_condition)
# If we couldn't do the mapping or the mapped site isn't invalid
# then we don't need to change the existing ModCondition
if not mapped_site or mapped_site.not_invalid():
continue
# Otherwise, if there is a mapping, we replace the old ModCondition
# with the new one where only the residue and position are updated,
# the mod type and the is modified flag are kept.
if mapped_site.has_mapping():
mc = ModCondition(mod_condition.mod_type,
mapped_site.mapped_res,
mapped_site.mapped_pos,
mod_condition.is_modified)
new_agent.mods[idx] = mc
# Finally, whether or not we have a mapping, we keep track of mapped
# sites and make them available to the caller
mapped_sites.append(mapped_site)
return mapped_sites, new_agent | [
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Parameters
----------
agent : :py:class:`indra.statements.Agent`
Agent to check for invalid modification sites.
Returns
-------
tuple
The first element is a list of MappedSite objects, the second
element is either the original Agent, if unchanged, or a copy
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18,854 | sorgerlab/indra | indra/preassembler/sitemapper.py | SiteMapper._map_agent_mod | def _map_agent_mod(self, agent, mod_condition):
"""Map a single modification condition on an agent.
Parameters
----------
agent : :py:class:`indra.statements.Agent`
Agent to check for invalid modification sites.
mod_condition : :py:class:`indra.statements.ModCondition`
Modification to check for validity and map.
Returns
-------
protmapper.MappedSite or None
A MappedSite object is returned if a UniProt ID was found for the
agent, and if both the position and residue for the modification
condition were available. Otherwise None is returned.
"""
# Get the UniProt ID of the agent, if not found, return
up_id = _get_uniprot_id(agent)
if not up_id:
logger.debug("No uniprot ID for %s" % agent.name)
return None
# If no site information for this residue, skip
if mod_condition.position is None or mod_condition.residue is None:
return None
# Otherwise, try to map it and return the mapped site
mapped_site = \
self.map_to_human_ref(up_id, 'uniprot',
mod_condition.residue,
mod_condition.position,
do_methionine_offset=self.do_methionine_offset,
do_orthology_mapping=self.do_orthology_mapping,
do_isoform_mapping=self.do_isoform_mapping)
return mapped_site | python | def _map_agent_mod(self, agent, mod_condition):
# Get the UniProt ID of the agent, if not found, return
up_id = _get_uniprot_id(agent)
if not up_id:
logger.debug("No uniprot ID for %s" % agent.name)
return None
# If no site information for this residue, skip
if mod_condition.position is None or mod_condition.residue is None:
return None
# Otherwise, try to map it and return the mapped site
mapped_site = \
self.map_to_human_ref(up_id, 'uniprot',
mod_condition.residue,
mod_condition.position,
do_methionine_offset=self.do_methionine_offset,
do_orthology_mapping=self.do_orthology_mapping,
do_isoform_mapping=self.do_isoform_mapping)
return mapped_site | [
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Agent to check for invalid modification sites.
mod_condition : :py:class:`indra.statements.ModCondition`
Modification to check for validity and map.
Returns
-------
protmapper.MappedSite or None
A MappedSite object is returned if a UniProt ID was found for the
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18,855 | sorgerlab/indra | indra/mechlinker/__init__.py | _get_graph_reductions | def _get_graph_reductions(graph):
"""Return transitive reductions on a DAG.
This is used to reduce the set of activities of a BaseAgent to the most
specific one(s) possible. For instance, if a BaseAgent is know to have
'activity', 'catalytic' and 'kinase' activity, then this function will
return {'activity': 'kinase', 'catalytic': 'kinase', 'kinase': 'kinase'}
as the set of reductions.
"""
def frontier(g, nd):
"""Return the nodes after nd in the topological sort that are at the
lowest possible level of the topological sort."""
if g.out_degree(nd) == 0:
return set([nd])
else:
frontiers = set()
for n in g.successors(nd):
frontiers = frontiers.union(frontier(graph, n))
return frontiers
reductions = {}
nodes_sort = list(networkx.algorithms.dag.topological_sort(graph))
frontiers = [frontier(graph, n) for n in nodes_sort]
# This loop ensures that if a node n2 comes after node n1 in the topological
# sort, and their frontiers are identical then n1 can be reduced to n2.
# If their frontiers aren't identical, the reduction cannot be done.
for i, n1 in enumerate(nodes_sort):
for j, n2 in enumerate(nodes_sort):
if i > j:
continue
if frontiers[i] == frontiers[j]:
reductions[n1] = n2
return reductions | python | def _get_graph_reductions(graph):
def frontier(g, nd):
"""Return the nodes after nd in the topological sort that are at the
lowest possible level of the topological sort."""
if g.out_degree(nd) == 0:
return set([nd])
else:
frontiers = set()
for n in g.successors(nd):
frontiers = frontiers.union(frontier(graph, n))
return frontiers
reductions = {}
nodes_sort = list(networkx.algorithms.dag.topological_sort(graph))
frontiers = [frontier(graph, n) for n in nodes_sort]
# This loop ensures that if a node n2 comes after node n1 in the topological
# sort, and their frontiers are identical then n1 can be reduced to n2.
# If their frontiers aren't identical, the reduction cannot be done.
for i, n1 in enumerate(nodes_sort):
for j, n2 in enumerate(nodes_sort):
if i > j:
continue
if frontiers[i] == frontiers[j]:
reductions[n1] = n2
return reductions | [
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'activity', 'catalytic' and 'kinase' activity, then this function will
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18,856 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.gather_explicit_activities | def gather_explicit_activities(self):
"""Aggregate all explicit activities and active forms of Agents.
This function iterates over self.statements and extracts explicitly
stated activity types and active forms for Agents.
"""
for stmt in self.statements:
agents = stmt.agent_list()
# Activity types given as ActivityConditions
for agent in agents:
if agent is not None and agent.activity is not None:
agent_base = self._get_base(agent)
agent_base.add_activity(agent.activity.activity_type)
# Object activities given in RegulateActivity statements
if isinstance(stmt, RegulateActivity):
if stmt.obj is not None:
obj_base = self._get_base(stmt.obj)
obj_base.add_activity(stmt.obj_activity)
# Activity types given in ActiveForms
elif isinstance(stmt, ActiveForm):
agent_base = self._get_base(stmt.agent)
agent_base.add_activity(stmt.activity)
if stmt.is_active:
agent_base.add_active_state(stmt.activity, stmt.agent,
stmt.evidence)
else:
agent_base.add_inactive_state(stmt.activity, stmt.agent,
stmt.evidence) | python | def gather_explicit_activities(self):
for stmt in self.statements:
agents = stmt.agent_list()
# Activity types given as ActivityConditions
for agent in agents:
if agent is not None and agent.activity is not None:
agent_base = self._get_base(agent)
agent_base.add_activity(agent.activity.activity_type)
# Object activities given in RegulateActivity statements
if isinstance(stmt, RegulateActivity):
if stmt.obj is not None:
obj_base = self._get_base(stmt.obj)
obj_base.add_activity(stmt.obj_activity)
# Activity types given in ActiveForms
elif isinstance(stmt, ActiveForm):
agent_base = self._get_base(stmt.agent)
agent_base.add_activity(stmt.activity)
if stmt.is_active:
agent_base.add_active_state(stmt.activity, stmt.agent,
stmt.evidence)
else:
agent_base.add_inactive_state(stmt.activity, stmt.agent,
stmt.evidence) | [
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18,857 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.gather_implicit_activities | def gather_implicit_activities(self):
"""Aggregate all implicit activities and active forms of Agents.
Iterate over self.statements and collect the implied activities
and active forms of Agents that appear in the Statements.
Note that using this function to collect implied Agent activities can
be risky. Assume, for instance, that a Statement from a reading
system states that EGF bound to EGFR phosphorylates ERK. This would
be interpreted as implicit evidence for the EGFR-bound form of EGF
to have 'kinase' activity, which is clearly incorrect.
In contrast the alternative pair of this function:
gather_explicit_activities collects only explicitly stated activities.
"""
for stmt in self.statements:
if isinstance(stmt, Phosphorylation) or \
isinstance(stmt, Transphosphorylation) or \
isinstance(stmt, Autophosphorylation):
if stmt.enz is not None:
enz_base = self._get_base(stmt.enz)
enz_base.add_activity('kinase')
enz_base.add_active_state('kinase', stmt.enz.mods)
elif isinstance(stmt, Dephosphorylation):
if stmt.enz is not None:
enz_base = self._get_base(stmt.enz)
enz_base.add_activity('phosphatase')
enz_base.add_active_state('phosphatase', stmt.enz.mods)
elif isinstance(stmt, Modification):
if stmt.enz is not None:
enz_base = self._get_base(stmt.enz)
enz_base.add_activity('catalytic')
enz_base.add_active_state('catalytic', stmt.enz.mods)
elif isinstance(stmt, SelfModification):
if stmt.enz is not None:
enz_base = self._get_base(stmt.enz)
enz_base.add_activity('catalytic')
enz_base.add_active_state('catalytic', stmt.enz.mods)
elif isinstance(stmt, Gef):
if stmt.gef is not None:
gef_base = self._get_base(stmt.gef)
gef_base.add_activity('gef')
if stmt.gef.activity is not None:
act = stmt.gef.activity.activity_type
else:
act = 'activity'
gef_base.add_active_state(act, stmt.gef.mods)
elif isinstance(stmt, Gap):
if stmt.gap is not None:
gap_base = self._get_base(stmt.gap)
gap_base.add_activity('gap')
if stmt.gap.activity is not None:
act = stmt.gap.activity.activity_type
else:
act = 'activity'
gap_base.add_active_state('act', stmt.gap.mods)
elif isinstance(stmt, RegulateActivity):
if stmt.subj is not None:
subj_base = self._get_base(stmt.subj)
subj_base.add_activity(stmt.j) | python | def gather_implicit_activities(self):
for stmt in self.statements:
if isinstance(stmt, Phosphorylation) or \
isinstance(stmt, Transphosphorylation) or \
isinstance(stmt, Autophosphorylation):
if stmt.enz is not None:
enz_base = self._get_base(stmt.enz)
enz_base.add_activity('kinase')
enz_base.add_active_state('kinase', stmt.enz.mods)
elif isinstance(stmt, Dephosphorylation):
if stmt.enz is not None:
enz_base = self._get_base(stmt.enz)
enz_base.add_activity('phosphatase')
enz_base.add_active_state('phosphatase', stmt.enz.mods)
elif isinstance(stmt, Modification):
if stmt.enz is not None:
enz_base = self._get_base(stmt.enz)
enz_base.add_activity('catalytic')
enz_base.add_active_state('catalytic', stmt.enz.mods)
elif isinstance(stmt, SelfModification):
if stmt.enz is not None:
enz_base = self._get_base(stmt.enz)
enz_base.add_activity('catalytic')
enz_base.add_active_state('catalytic', stmt.enz.mods)
elif isinstance(stmt, Gef):
if stmt.gef is not None:
gef_base = self._get_base(stmt.gef)
gef_base.add_activity('gef')
if stmt.gef.activity is not None:
act = stmt.gef.activity.activity_type
else:
act = 'activity'
gef_base.add_active_state(act, stmt.gef.mods)
elif isinstance(stmt, Gap):
if stmt.gap is not None:
gap_base = self._get_base(stmt.gap)
gap_base.add_activity('gap')
if stmt.gap.activity is not None:
act = stmt.gap.activity.activity_type
else:
act = 'activity'
gap_base.add_active_state('act', stmt.gap.mods)
elif isinstance(stmt, RegulateActivity):
if stmt.subj is not None:
subj_base = self._get_base(stmt.subj)
subj_base.add_activity(stmt.j) | [
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Note that using this function to collect implied Agent activities can
be risky. Assume, for instance, that a Statement from a reading
system states that EGF bound to EGFR phosphorylates ERK. This would
be interpreted as implicit evidence for the EGFR-bound form of EGF
to have 'kinase' activity, which is clearly incorrect.
In contrast the alternative pair of this function:
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18,858 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.require_active_forms | def require_active_forms(self):
"""Rewrites Statements with Agents' active forms in active positions.
As an example, the enzyme in a Modification Statement can be expected
to be in an active state. Similarly, subjects of RegulateAmount and
RegulateActivity Statements can be expected to be in an active form.
This function takes the collected active states of Agents in their
corresponding BaseAgents and then rewrites other Statements to apply
the active Agent states to them.
Returns
-------
new_stmts : list[indra.statements.Statement]
A list of Statements which includes the newly rewritten Statements.
This list is also set as the internal Statement list of the
MechLinker.
"""
logger.info('Setting required active forms on %d statements...' %
len(self.statements))
new_stmts = []
for stmt in self.statements:
if isinstance(stmt, Modification):
if stmt.enz is None:
new_stmts.append(stmt)
continue
enz_base = self._get_base(stmt.enz)
active_forms = enz_base.get_active_forms()
if not active_forms:
new_stmts.append(stmt)
else:
for af in active_forms:
new_stmt = fast_deepcopy(stmt)
new_stmt.uuid = str(uuid.uuid4())
evs = af.apply_to(new_stmt.enz)
new_stmt.partial_evidence = evs
new_stmts.append(new_stmt)
elif isinstance(stmt, RegulateAmount) or \
isinstance(stmt, RegulateActivity):
if stmt.subj is None:
new_stmts.append(stmt)
continue
subj_base = self._get_base(stmt.subj)
active_forms = subj_base.get_active_forms()
if not active_forms:
new_stmts.append(stmt)
else:
for af in active_forms:
new_stmt = fast_deepcopy(stmt)
new_stmt.uuid = str(uuid.uuid4())
evs = af.apply_to(new_stmt.subj)
new_stmt.partial_evidence = evs
new_stmts.append(new_stmt)
else:
new_stmts.append(stmt)
self.statements = new_stmts
return new_stmts | python | def require_active_forms(self):
logger.info('Setting required active forms on %d statements...' %
len(self.statements))
new_stmts = []
for stmt in self.statements:
if isinstance(stmt, Modification):
if stmt.enz is None:
new_stmts.append(stmt)
continue
enz_base = self._get_base(stmt.enz)
active_forms = enz_base.get_active_forms()
if not active_forms:
new_stmts.append(stmt)
else:
for af in active_forms:
new_stmt = fast_deepcopy(stmt)
new_stmt.uuid = str(uuid.uuid4())
evs = af.apply_to(new_stmt.enz)
new_stmt.partial_evidence = evs
new_stmts.append(new_stmt)
elif isinstance(stmt, RegulateAmount) or \
isinstance(stmt, RegulateActivity):
if stmt.subj is None:
new_stmts.append(stmt)
continue
subj_base = self._get_base(stmt.subj)
active_forms = subj_base.get_active_forms()
if not active_forms:
new_stmts.append(stmt)
else:
for af in active_forms:
new_stmt = fast_deepcopy(stmt)
new_stmt.uuid = str(uuid.uuid4())
evs = af.apply_to(new_stmt.subj)
new_stmt.partial_evidence = evs
new_stmts.append(new_stmt)
else:
new_stmts.append(stmt)
self.statements = new_stmts
return new_stmts | [
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As an example, the enzyme in a Modification Statement can be expected
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RegulateActivity Statements can be expected to be in an active form.
This function takes the collected active states of Agents in their
corresponding BaseAgents and then rewrites other Statements to apply
the active Agent states to them.
Returns
-------
new_stmts : list[indra.statements.Statement]
A list of Statements which includes the newly rewritten Statements.
This list is also set as the internal Statement list of the
MechLinker. | [
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18,859 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.reduce_activities | def reduce_activities(self):
"""Rewrite the activity types referenced in Statements for consistency.
Activity types are reduced to the most specific form whenever possible.
For instance, if 'kinase' is the only specific activity type known
for the BaseAgent of BRAF, its generic 'activity' forms are rewritten
to 'kinase'.
"""
for stmt in self.statements:
agents = stmt.agent_list()
for agent in agents:
if agent is not None and agent.activity is not None:
agent_base = self._get_base(agent)
act_red = agent_base.get_activity_reduction(
agent.activity.activity_type)
if act_red is not None:
agent.activity.activity_type = act_red
if isinstance(stmt, RegulateActivity):
if stmt.obj is not None:
obj_base = self._get_base(stmt.obj)
act_red = \
obj_base.get_activity_reduction(stmt.obj_activity)
if act_red is not None:
stmt.obj_activity = act_red
elif isinstance(stmt, ActiveForm):
agent_base = self._get_base(stmt.agent)
act_red = agent_base.get_activity_reduction(stmt.activity)
if act_red is not None:
stmt.activity = act_red | python | def reduce_activities(self):
for stmt in self.statements:
agents = stmt.agent_list()
for agent in agents:
if agent is not None and agent.activity is not None:
agent_base = self._get_base(agent)
act_red = agent_base.get_activity_reduction(
agent.activity.activity_type)
if act_red is not None:
agent.activity.activity_type = act_red
if isinstance(stmt, RegulateActivity):
if stmt.obj is not None:
obj_base = self._get_base(stmt.obj)
act_red = \
obj_base.get_activity_reduction(stmt.obj_activity)
if act_red is not None:
stmt.obj_activity = act_red
elif isinstance(stmt, ActiveForm):
agent_base = self._get_base(stmt.agent)
act_red = agent_base.get_activity_reduction(stmt.activity)
if act_red is not None:
stmt.activity = act_red | [
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Activity types are reduced to the most specific form whenever possible.
For instance, if 'kinase' is the only specific activity type known
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18,860 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.infer_complexes | def infer_complexes(stmts):
"""Return inferred Complex from Statements implying physical interaction.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of Statements to infer Complexes from.
Returns
-------
linked_stmts : list[indra.mechlinker.LinkedStatement]
A list of LinkedStatements representing the inferred Statements.
"""
interact_stmts = _get_statements_by_type(stmts, Modification)
linked_stmts = []
for mstmt in interact_stmts:
if mstmt.enz is None:
continue
st = Complex([mstmt.enz, mstmt.sub], evidence=mstmt.evidence)
linked_stmts.append(st)
return linked_stmts | python | def infer_complexes(stmts):
interact_stmts = _get_statements_by_type(stmts, Modification)
linked_stmts = []
for mstmt in interact_stmts:
if mstmt.enz is None:
continue
st = Complex([mstmt.enz, mstmt.sub], evidence=mstmt.evidence)
linked_stmts.append(st)
return linked_stmts | [
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stmts : list[indra.statements.Statement]
A list of Statements to infer Complexes from.
Returns
-------
linked_stmts : list[indra.mechlinker.LinkedStatement]
A list of LinkedStatements representing the inferred Statements. | [
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18,861 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.infer_activations | def infer_activations(stmts):
"""Return inferred RegulateActivity from Modification + ActiveForm.
This function looks for combinations of Modification and ActiveForm
Statements and infers Activation/Inhibition Statements from them.
For example, if we know that A phosphorylates B, and the
phosphorylated form of B is active, then we can infer that
A activates B. This can also be viewed as having "explained" a given
Activation/Inhibition Statement with a combination of more mechanistic
Modification + ActiveForm Statements.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of Statements to infer RegulateActivity from.
Returns
-------
linked_stmts : list[indra.mechlinker.LinkedStatement]
A list of LinkedStatements representing the inferred Statements.
"""
linked_stmts = []
af_stmts = _get_statements_by_type(stmts, ActiveForm)
mod_stmts = _get_statements_by_type(stmts, Modification)
for af_stmt, mod_stmt in itertools.product(*(af_stmts, mod_stmts)):
# There has to be an enzyme and the substrate and the
# agent of the active form have to match
if mod_stmt.enz is None or \
(not af_stmt.agent.entity_matches(mod_stmt.sub)):
continue
# We now check the modifications to make sure they are consistent
if not af_stmt.agent.mods:
continue
found = False
for mc in af_stmt.agent.mods:
if mc.mod_type == modclass_to_modtype[mod_stmt.__class__] and \
mc.residue == mod_stmt.residue and \
mc.position == mod_stmt.position:
found = True
if not found:
continue
# Collect evidence
ev = mod_stmt.evidence
# Finally, check the polarity of the ActiveForm
if af_stmt.is_active:
st = Activation(mod_stmt.enz, mod_stmt.sub, af_stmt.activity,
evidence=ev)
else:
st = Inhibition(mod_stmt.enz, mod_stmt.sub, af_stmt.activity,
evidence=ev)
linked_stmts.append(LinkedStatement([af_stmt, mod_stmt], st))
return linked_stmts | python | def infer_activations(stmts):
linked_stmts = []
af_stmts = _get_statements_by_type(stmts, ActiveForm)
mod_stmts = _get_statements_by_type(stmts, Modification)
for af_stmt, mod_stmt in itertools.product(*(af_stmts, mod_stmts)):
# There has to be an enzyme and the substrate and the
# agent of the active form have to match
if mod_stmt.enz is None or \
(not af_stmt.agent.entity_matches(mod_stmt.sub)):
continue
# We now check the modifications to make sure they are consistent
if not af_stmt.agent.mods:
continue
found = False
for mc in af_stmt.agent.mods:
if mc.mod_type == modclass_to_modtype[mod_stmt.__class__] and \
mc.residue == mod_stmt.residue and \
mc.position == mod_stmt.position:
found = True
if not found:
continue
# Collect evidence
ev = mod_stmt.evidence
# Finally, check the polarity of the ActiveForm
if af_stmt.is_active:
st = Activation(mod_stmt.enz, mod_stmt.sub, af_stmt.activity,
evidence=ev)
else:
st = Inhibition(mod_stmt.enz, mod_stmt.sub, af_stmt.activity,
evidence=ev)
linked_stmts.append(LinkedStatement([af_stmt, mod_stmt], st))
return linked_stmts | [
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This function looks for combinations of Modification and ActiveForm
Statements and infers Activation/Inhibition Statements from them.
For example, if we know that A phosphorylates B, and the
phosphorylated form of B is active, then we can infer that
A activates B. This can also be viewed as having "explained" a given
Activation/Inhibition Statement with a combination of more mechanistic
Modification + ActiveForm Statements.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of Statements to infer RegulateActivity from.
Returns
-------
linked_stmts : list[indra.mechlinker.LinkedStatement]
A list of LinkedStatements representing the inferred Statements. | [
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18,862 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.infer_active_forms | def infer_active_forms(stmts):
"""Return inferred ActiveForm from RegulateActivity + Modification.
This function looks for combinations of Activation/Inhibition
Statements and Modification Statements, and infers an ActiveForm
from them. For example, if we know that A activates B and
A phosphorylates B, then we can infer that the phosphorylated form
of B is active.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of Statements to infer ActiveForms from.
Returns
-------
linked_stmts : list[indra.mechlinker.LinkedStatement]
A list of LinkedStatements representing the inferred Statements.
"""
linked_stmts = []
for act_stmt in _get_statements_by_type(stmts, RegulateActivity):
# TODO: revise the conditions here
if not (act_stmt.subj.activity is not None and
act_stmt.subj.activity.activity_type == 'kinase' and
act_stmt.subj.activity.is_active):
continue
matching = []
ev = act_stmt.evidence
for mod_stmt in _get_statements_by_type(stmts, Modification):
if mod_stmt.enz is not None:
if mod_stmt.enz.entity_matches(act_stmt.subj) and \
mod_stmt.sub.entity_matches(act_stmt.obj):
matching.append(mod_stmt)
ev.extend(mod_stmt.evidence)
if not matching:
continue
mods = []
for mod_stmt in matching:
mod_type_name = mod_stmt.__class__.__name__.lower()
if isinstance(mod_stmt, AddModification):
is_modified = True
else:
is_modified = False
mod_type_name = mod_type_name[2:]
mc = ModCondition(mod_type_name, mod_stmt.residue,
mod_stmt.position, is_modified)
mods.append(mc)
source_stmts = [act_stmt] + [m for m in matching]
st = ActiveForm(Agent(act_stmt.obj.name, mods=mods,
db_refs=act_stmt.obj.db_refs),
act_stmt.obj_activity, act_stmt.is_activation,
evidence=ev)
linked_stmts.append(LinkedStatement(source_stmts, st))
logger.info('inferred: %s' % st)
return linked_stmts | python | def infer_active_forms(stmts):
linked_stmts = []
for act_stmt in _get_statements_by_type(stmts, RegulateActivity):
# TODO: revise the conditions here
if not (act_stmt.subj.activity is not None and
act_stmt.subj.activity.activity_type == 'kinase' and
act_stmt.subj.activity.is_active):
continue
matching = []
ev = act_stmt.evidence
for mod_stmt in _get_statements_by_type(stmts, Modification):
if mod_stmt.enz is not None:
if mod_stmt.enz.entity_matches(act_stmt.subj) and \
mod_stmt.sub.entity_matches(act_stmt.obj):
matching.append(mod_stmt)
ev.extend(mod_stmt.evidence)
if not matching:
continue
mods = []
for mod_stmt in matching:
mod_type_name = mod_stmt.__class__.__name__.lower()
if isinstance(mod_stmt, AddModification):
is_modified = True
else:
is_modified = False
mod_type_name = mod_type_name[2:]
mc = ModCondition(mod_type_name, mod_stmt.residue,
mod_stmt.position, is_modified)
mods.append(mc)
source_stmts = [act_stmt] + [m for m in matching]
st = ActiveForm(Agent(act_stmt.obj.name, mods=mods,
db_refs=act_stmt.obj.db_refs),
act_stmt.obj_activity, act_stmt.is_activation,
evidence=ev)
linked_stmts.append(LinkedStatement(source_stmts, st))
logger.info('inferred: %s' % st)
return linked_stmts | [
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This function looks for combinations of Activation/Inhibition
Statements and Modification Statements, and infers an ActiveForm
from them. For example, if we know that A activates B and
A phosphorylates B, then we can infer that the phosphorylated form
of B is active.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of Statements to infer ActiveForms from.
Returns
-------
linked_stmts : list[indra.mechlinker.LinkedStatement]
A list of LinkedStatements representing the inferred Statements. | [
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18,863 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.infer_modifications | def infer_modifications(stmts):
"""Return inferred Modification from RegulateActivity + ActiveForm.
This function looks for combinations of Activation/Inhibition Statements
and ActiveForm Statements that imply a Modification Statement.
For example, if we know that A activates B, and phosphorylated B is
active, then we can infer that A leads to the phosphorylation of B.
An additional requirement when making this assumption is that the
activity of B should only be dependent on the modified state and not
other context - otherwise the inferred Modification is not necessarily
warranted.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of Statements to infer Modifications from.
Returns
-------
linked_stmts : list[indra.mechlinker.LinkedStatement]
A list of LinkedStatements representing the inferred Statements.
"""
linked_stmts = []
for act_stmt in _get_statements_by_type(stmts, RegulateActivity):
for af_stmt in _get_statements_by_type(stmts, ActiveForm):
if not af_stmt.agent.entity_matches(act_stmt.obj):
continue
mods = af_stmt.agent.mods
# Make sure the ActiveForm only involves modified sites
if af_stmt.agent.mutations or \
af_stmt.agent.bound_conditions or \
af_stmt.agent.location:
continue
if not af_stmt.agent.mods:
continue
for mod in af_stmt.agent.mods:
evs = act_stmt.evidence + af_stmt.evidence
for ev in evs:
ev.epistemics['direct'] = False
if mod.is_modified:
mod_type_name = mod.mod_type
else:
mod_type_name = modtype_to_inverse[mod.mod_type]
mod_class = modtype_to_modclass[mod_type_name]
if not mod_class:
continue
st = mod_class(act_stmt.subj,
act_stmt.obj,
mod.residue, mod.position,
evidence=evs)
ls = LinkedStatement([act_stmt, af_stmt], st)
linked_stmts.append(ls)
logger.info('inferred: %s' % st)
return linked_stmts | python | def infer_modifications(stmts):
linked_stmts = []
for act_stmt in _get_statements_by_type(stmts, RegulateActivity):
for af_stmt in _get_statements_by_type(stmts, ActiveForm):
if not af_stmt.agent.entity_matches(act_stmt.obj):
continue
mods = af_stmt.agent.mods
# Make sure the ActiveForm only involves modified sites
if af_stmt.agent.mutations or \
af_stmt.agent.bound_conditions or \
af_stmt.agent.location:
continue
if not af_stmt.agent.mods:
continue
for mod in af_stmt.agent.mods:
evs = act_stmt.evidence + af_stmt.evidence
for ev in evs:
ev.epistemics['direct'] = False
if mod.is_modified:
mod_type_name = mod.mod_type
else:
mod_type_name = modtype_to_inverse[mod.mod_type]
mod_class = modtype_to_modclass[mod_type_name]
if not mod_class:
continue
st = mod_class(act_stmt.subj,
act_stmt.obj,
mod.residue, mod.position,
evidence=evs)
ls = LinkedStatement([act_stmt, af_stmt], st)
linked_stmts.append(ls)
logger.info('inferred: %s' % st)
return linked_stmts | [
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This function looks for combinations of Activation/Inhibition Statements
and ActiveForm Statements that imply a Modification Statement.
For example, if we know that A activates B, and phosphorylated B is
active, then we can infer that A leads to the phosphorylation of B.
An additional requirement when making this assumption is that the
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other context - otherwise the inferred Modification is not necessarily
warranted.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of Statements to infer Modifications from.
Returns
-------
linked_stmts : list[indra.mechlinker.LinkedStatement]
A list of LinkedStatements representing the inferred Statements. | [
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18,864 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.replace_complexes | def replace_complexes(self, linked_stmts=None):
"""Remove Complex Statements that can be inferred out.
This function iterates over self.statements and looks for Complex
Statements that either match or are refined by inferred Complex
Statements that were linked (provided as the linked_stmts argument).
It removes Complex Statements from self.statements that can be
explained by the linked statements.
Parameters
----------
linked_stmts : Optional[list[indra.mechlinker.LinkedStatement]]
A list of linked statements, optionally passed from outside.
If None is passed, the MechLinker runs self.infer_complexes to
infer Complexes and obtain a list of LinkedStatements that are
then used for removing existing Complexes in self.statements.
"""
if linked_stmts is None:
linked_stmts = self.infer_complexes(self.statements)
new_stmts = []
for stmt in self.statements:
if not isinstance(stmt, Complex):
new_stmts.append(stmt)
continue
found = False
for linked_stmt in linked_stmts:
if linked_stmt.refinement_of(stmt, hierarchies):
found = True
if not found:
new_stmts.append(stmt)
else:
logger.info('Removing complex: %s' % stmt)
self.statements = new_stmts | python | def replace_complexes(self, linked_stmts=None):
if linked_stmts is None:
linked_stmts = self.infer_complexes(self.statements)
new_stmts = []
for stmt in self.statements:
if not isinstance(stmt, Complex):
new_stmts.append(stmt)
continue
found = False
for linked_stmt in linked_stmts:
if linked_stmt.refinement_of(stmt, hierarchies):
found = True
if not found:
new_stmts.append(stmt)
else:
logger.info('Removing complex: %s' % stmt)
self.statements = new_stmts | [
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It removes Complex Statements from self.statements that can be
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Parameters
----------
linked_stmts : Optional[list[indra.mechlinker.LinkedStatement]]
A list of linked statements, optionally passed from outside.
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18,865 | sorgerlab/indra | indra/mechlinker/__init__.py | MechLinker.replace_activations | def replace_activations(self, linked_stmts=None):
"""Remove RegulateActivity Statements that can be inferred out.
This function iterates over self.statements and looks for
RegulateActivity Statements that either match or are refined by
inferred RegulateActivity Statements that were linked
(provided as the linked_stmts argument).
It removes RegulateActivity Statements from self.statements that can be
explained by the linked statements.
Parameters
----------
linked_stmts : Optional[list[indra.mechlinker.LinkedStatement]]
A list of linked statements, optionally passed from outside.
If None is passed, the MechLinker runs self.infer_activations to
infer RegulateActivities and obtain a list of LinkedStatements
that are then used for removing existing Complexes
in self.statements.
"""
if linked_stmts is None:
linked_stmts = self.infer_activations(self.statements)
new_stmts = []
for stmt in self.statements:
if not isinstance(stmt, RegulateActivity):
new_stmts.append(stmt)
continue
found = False
for linked_stmt in linked_stmts:
inferred_stmt = linked_stmt.inferred_stmt
if stmt.is_activation == inferred_stmt.is_activation and \
stmt.subj.entity_matches(inferred_stmt.subj) and \
stmt.obj.entity_matches(inferred_stmt.obj):
found = True
if not found:
new_stmts.append(stmt)
else:
logger.info('Removing regulate activity: %s' % stmt)
self.statements = new_stmts | python | def replace_activations(self, linked_stmts=None):
if linked_stmts is None:
linked_stmts = self.infer_activations(self.statements)
new_stmts = []
for stmt in self.statements:
if not isinstance(stmt, RegulateActivity):
new_stmts.append(stmt)
continue
found = False
for linked_stmt in linked_stmts:
inferred_stmt = linked_stmt.inferred_stmt
if stmt.is_activation == inferred_stmt.is_activation and \
stmt.subj.entity_matches(inferred_stmt.subj) and \
stmt.obj.entity_matches(inferred_stmt.obj):
found = True
if not found:
new_stmts.append(stmt)
else:
logger.info('Removing regulate activity: %s' % stmt)
self.statements = new_stmts | [
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It removes RegulateActivity Statements from self.statements that can be
explained by the linked statements.
Parameters
----------
linked_stmts : Optional[list[indra.mechlinker.LinkedStatement]]
A list of linked statements, optionally passed from outside.
If None is passed, the MechLinker runs self.infer_activations to
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18,866 | sorgerlab/indra | indra/mechlinker/__init__.py | BaseAgentSet.get_create_base_agent | def get_create_base_agent(self, agent):
"""Return BaseAgent from an Agent, creating it if needed.
Parameters
----------
agent : indra.statements.Agent
Returns
-------
base_agent : indra.mechlinker.BaseAgent
"""
try:
base_agent = self.agents[agent.name]
except KeyError:
base_agent = BaseAgent(agent.name)
self.agents[agent.name] = base_agent
return base_agent | python | def get_create_base_agent(self, agent):
try:
base_agent = self.agents[agent.name]
except KeyError:
base_agent = BaseAgent(agent.name)
self.agents[agent.name] = base_agent
return base_agent | [
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18,867 | sorgerlab/indra | indra/mechlinker/__init__.py | AgentState.apply_to | def apply_to(self, agent):
"""Apply this object's state to an Agent.
Parameters
----------
agent : indra.statements.Agent
The agent to which the state should be applied
"""
agent.bound_conditions = self.bound_conditions
agent.mods = self.mods
agent.mutations = self.mutations
agent.location = self.location
return self.evidence | python | def apply_to(self, agent):
agent.bound_conditions = self.bound_conditions
agent.mods = self.mods
agent.mutations = self.mutations
agent.location = self.location
return self.evidence | [
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18,868 | sorgerlab/indra | indra/tools/live_curation.py | submit_curation | def submit_curation():
"""Submit curations for a given corpus.
The submitted curations are handled to update the probability model but
there is no return value here. The update_belief function can be called
separately to calculate update belief scores.
Parameters
----------
corpus_id : str
The ID of the corpus for which the curation is submitted.
curations : dict
A set of curations where each key is a Statement UUID in the given
corpus and each key is 0 or 1 with 0 corresponding to incorrect and
1 corresponding to correct.
"""
if request.json is None:
abort(Response('Missing application/json header.', 415))
# Get input parameters
corpus_id = request.json.get('corpus_id')
curations = request.json.get('curations', {})
try:
curator.submit_curation(corpus_id, curations)
except InvalidCorpusError:
abort(Response('The corpus_id "%s" is unknown.' % corpus_id, 400))
return
return jsonify({}) | python | def submit_curation():
if request.json is None:
abort(Response('Missing application/json header.', 415))
# Get input parameters
corpus_id = request.json.get('corpus_id')
curations = request.json.get('curations', {})
try:
curator.submit_curation(corpus_id, curations)
except InvalidCorpusError:
abort(Response('The corpus_id "%s" is unknown.' % corpus_id, 400))
return
return jsonify({}) | [
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Parameters
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corpus_id : str
The ID of the corpus for which the curation is submitted.
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18,869 | sorgerlab/indra | indra/tools/live_curation.py | update_beliefs | def update_beliefs():
"""Return updated beliefs based on current probability model."""
if request.json is None:
abort(Response('Missing application/json header.', 415))
# Get input parameters
corpus_id = request.json.get('corpus_id')
try:
belief_dict = curator.update_beliefs(corpus_id)
except InvalidCorpusError:
abort(Response('The corpus_id "%s" is unknown.' % corpus_id, 400))
return
return jsonify(belief_dict) | python | def update_beliefs():
if request.json is None:
abort(Response('Missing application/json header.', 415))
# Get input parameters
corpus_id = request.json.get('corpus_id')
try:
belief_dict = curator.update_beliefs(corpus_id)
except InvalidCorpusError:
abort(Response('The corpus_id "%s" is unknown.' % corpus_id, 400))
return
return jsonify(belief_dict) | [
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18,870 | sorgerlab/indra | indra/tools/live_curation.py | LiveCurator.reset_scorer | def reset_scorer(self):
"""Reset the scorer used for couration."""
self.scorer = get_eidos_bayesian_scorer()
for corpus_id, corpus in self.corpora.items():
corpus.curations = {} | python | def reset_scorer(self):
self.scorer = get_eidos_bayesian_scorer()
for corpus_id, corpus in self.corpora.items():
corpus.curations = {} | [
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18,871 | sorgerlab/indra | indra/tools/live_curation.py | LiveCurator.get_corpus | def get_corpus(self, corpus_id):
"""Return a corpus given an ID.
If the corpus ID cannot be found, an InvalidCorpusError is raised.
Parameters
----------
corpus_id : str
The ID of the corpus to return.
Returns
-------
Corpus
The corpus with the given ID.
"""
try:
corpus = self.corpora[corpus_id]
return corpus
except KeyError:
raise InvalidCorpusError | python | def get_corpus(self, corpus_id):
try:
corpus = self.corpora[corpus_id]
return corpus
except KeyError:
raise InvalidCorpusError | [
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If the corpus ID cannot be found, an InvalidCorpusError is raised.
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corpus_id : str
The ID of the corpus to return.
Returns
-------
Corpus
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18,872 | sorgerlab/indra | indra/tools/live_curation.py | LiveCurator.update_beliefs | def update_beliefs(self, corpus_id):
"""Return updated belief scores for a given corpus.
Parameters
----------
corpus_id : str
The ID of the corpus for which beliefs are to be updated.
Returns
-------
dict
A dictionary of belief scores with keys corresponding to Statement
UUIDs and values to new belief scores.
"""
corpus = self.get_corpus(corpus_id)
be = BeliefEngine(self.scorer)
stmts = list(corpus.statements.values())
be.set_prior_probs(stmts)
# Here we set beliefs based on actual curation
for uuid, correct in corpus.curations.items():
stmt = corpus.statements.get(uuid)
if stmt is None:
logger.warning('%s is not in the corpus.' % uuid)
continue
stmt.belief = correct
belief_dict = {st.uuid: st.belief for st in stmts}
return belief_dict | python | def update_beliefs(self, corpus_id):
corpus = self.get_corpus(corpus_id)
be = BeliefEngine(self.scorer)
stmts = list(corpus.statements.values())
be.set_prior_probs(stmts)
# Here we set beliefs based on actual curation
for uuid, correct in corpus.curations.items():
stmt = corpus.statements.get(uuid)
if stmt is None:
logger.warning('%s is not in the corpus.' % uuid)
continue
stmt.belief = correct
belief_dict = {st.uuid: st.belief for st in stmts}
return belief_dict | [
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18,873 | sorgerlab/indra | indra/sources/eidos/scala_utils.py | get_python_list | def get_python_list(scala_list):
"""Return list from elements of scala.collection.immutable.List"""
python_list = []
for i in range(scala_list.length()):
python_list.append(scala_list.apply(i))
return python_list | python | def get_python_list(scala_list):
python_list = []
for i in range(scala_list.length()):
python_list.append(scala_list.apply(i))
return python_list | [
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18,874 | sorgerlab/indra | indra/sources/eidos/scala_utils.py | get_python_dict | def get_python_dict(scala_map):
"""Return a dict from entries in a scala.collection.immutable.Map"""
python_dict = {}
keys = get_python_list(scala_map.keys().toList())
for key in keys:
python_dict[key] = scala_map.apply(key)
return python_dict | python | def get_python_dict(scala_map):
python_dict = {}
keys = get_python_list(scala_map.keys().toList())
for key in keys:
python_dict[key] = scala_map.apply(key)
return python_dict | [
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18,875 | sorgerlab/indra | indra/sources/eidos/scala_utils.py | get_python_json | def get_python_json(scala_json):
"""Return a JSON dict from a org.json4s.JsonAST"""
def convert_node(node):
if node.__class__.__name__ in ('org.json4s.JsonAST$JValue',
'org.json4s.JsonAST$JObject'):
# Make a dictionary and then convert each value
values_raw = get_python_dict(node.values())
values = {}
for k, v in values_raw.items():
values[k] = convert_node(v)
return values
elif node.__class__.__name__.startswith('scala.collection.immutable.Map') or \
node.__class__.__name__ == \
'scala.collection.immutable.HashMap$HashTrieMap':
values_raw = get_python_dict(node)
values = {}
for k, v in values_raw.items():
values[k] = convert_node(v)
return values
elif node.__class__.__name__ == 'org.json4s.JsonAST$JArray':
entries_raw = get_python_list(node.values())
entries = []
for entry in entries_raw:
entries.append(convert_node(entry))
return entries
elif node.__class__.__name__ == 'scala.collection.immutable.$colon$colon':
entries_raw = get_python_list(node)
entries = []
for entry in entries_raw:
entries.append(convert_node(entry))
return entries
elif node.__class__.__name__ == 'scala.math.BigInt':
return node.intValue()
elif node.__class__.__name__ == 'scala.None$':
return None
elif node.__class__.__name__ == 'scala.collection.immutable.Nil$':
return []
elif isinstance(node, (str, int, float)):
return node
else:
logger.error('Cannot convert %s into Python' %
node.__class__.__name__)
return node.__class__.__name__
python_json = convert_node(scala_json)
return python_json | python | def get_python_json(scala_json):
def convert_node(node):
if node.__class__.__name__ in ('org.json4s.JsonAST$JValue',
'org.json4s.JsonAST$JObject'):
# Make a dictionary and then convert each value
values_raw = get_python_dict(node.values())
values = {}
for k, v in values_raw.items():
values[k] = convert_node(v)
return values
elif node.__class__.__name__.startswith('scala.collection.immutable.Map') or \
node.__class__.__name__ == \
'scala.collection.immutable.HashMap$HashTrieMap':
values_raw = get_python_dict(node)
values = {}
for k, v in values_raw.items():
values[k] = convert_node(v)
return values
elif node.__class__.__name__ == 'org.json4s.JsonAST$JArray':
entries_raw = get_python_list(node.values())
entries = []
for entry in entries_raw:
entries.append(convert_node(entry))
return entries
elif node.__class__.__name__ == 'scala.collection.immutable.$colon$colon':
entries_raw = get_python_list(node)
entries = []
for entry in entries_raw:
entries.append(convert_node(entry))
return entries
elif node.__class__.__name__ == 'scala.math.BigInt':
return node.intValue()
elif node.__class__.__name__ == 'scala.None$':
return None
elif node.__class__.__name__ == 'scala.collection.immutable.Nil$':
return []
elif isinstance(node, (str, int, float)):
return node
else:
logger.error('Cannot convert %s into Python' %
node.__class__.__name__)
return node.__class__.__name__
python_json = convert_node(scala_json)
return python_json | [
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18,876 | sorgerlab/indra | indra/databases/relevance_client.py | get_heat_kernel | def get_heat_kernel(network_id):
"""Return the identifier of a heat kernel calculated for a given network.
Parameters
----------
network_id : str
The UUID of the network in NDEx.
Returns
-------
kernel_id : str
The identifier of the heat kernel calculated for the given network.
"""
url = ndex_relevance + '/%s/generate_ndex_heat_kernel' % network_id
res = ndex_client.send_request(url, {}, is_json=True, use_get=True)
if res is None:
logger.error('Could not get heat kernel for network %s.' % network_id)
return None
kernel_id = res.get('kernel_id')
if kernel_id is None:
logger.error('Could not get heat kernel for network %s.' % network_id)
return None
return kernel_id | python | def get_heat_kernel(network_id):
url = ndex_relevance + '/%s/generate_ndex_heat_kernel' % network_id
res = ndex_client.send_request(url, {}, is_json=True, use_get=True)
if res is None:
logger.error('Could not get heat kernel for network %s.' % network_id)
return None
kernel_id = res.get('kernel_id')
if kernel_id is None:
logger.error('Could not get heat kernel for network %s.' % network_id)
return None
return kernel_id | [
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18,877 | sorgerlab/indra | indra/databases/relevance_client.py | get_relevant_nodes | def get_relevant_nodes(network_id, query_nodes):
"""Return a set of network nodes relevant to a given query set.
A heat diffusion algorithm is used on a pre-computed heat kernel for the
given network which starts from the given query nodes. The nodes
in the network are ranked according to heat score which is a measure
of relevance with respect to the query nodes.
Parameters
----------
network_id : str
The UUID of the network in NDEx.
query_nodes : list[str]
A list of node names with respect to which relevance is queried.
Returns
-------
ranked_entities : list[(str, float)]
A list containing pairs of node names and their relevance scores.
"""
url = ndex_relevance + '/rank_entities'
kernel_id = get_heat_kernel(network_id)
if kernel_id is None:
return None
if isinstance(query_nodes, basestring):
query_nodes = [query_nodes]
params = {'identifier_set': query_nodes,
'kernel_id': kernel_id}
res = ndex_client.send_request(url, params, is_json=True)
if res is None:
logger.error("ndex_client.send_request returned None.")
return None
ranked_entities = res.get('ranked_entities')
if ranked_entities is None:
logger.error('Could not get ranked entities.')
return None
return ranked_entities | python | def get_relevant_nodes(network_id, query_nodes):
url = ndex_relevance + '/rank_entities'
kernel_id = get_heat_kernel(network_id)
if kernel_id is None:
return None
if isinstance(query_nodes, basestring):
query_nodes = [query_nodes]
params = {'identifier_set': query_nodes,
'kernel_id': kernel_id}
res = ndex_client.send_request(url, params, is_json=True)
if res is None:
logger.error("ndex_client.send_request returned None.")
return None
ranked_entities = res.get('ranked_entities')
if ranked_entities is None:
logger.error('Could not get ranked entities.')
return None
return ranked_entities | [
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A heat diffusion algorithm is used on a pre-computed heat kernel for the
given network which starts from the given query nodes. The nodes
in the network are ranked according to heat score which is a measure
of relevance with respect to the query nodes.
Parameters
----------
network_id : str
The UUID of the network in NDEx.
query_nodes : list[str]
A list of node names with respect to which relevance is queried.
Returns
-------
ranked_entities : list[(str, float)]
A list containing pairs of node names and their relevance scores. | [
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18,878 | sorgerlab/indra | indra/belief/__init__.py | _get_belief_package | def _get_belief_package(stmt):
"""Return the belief packages of a given statement recursively."""
# This list will contain the belief packages for the given statement
belief_packages = []
# Iterate over all the support parents
for st in stmt.supports:
# Recursively get all the belief packages of the parent
parent_packages = _get_belief_package(st)
package_stmt_keys = [pkg.statement_key for pkg in belief_packages]
for package in parent_packages:
# Only add this belief package if it hasn't already been added
if package.statement_key not in package_stmt_keys:
belief_packages.append(package)
# Now make the Statement's own belief package and append it to the list
belief_package = BeliefPackage(stmt.matches_key(), stmt.evidence)
belief_packages.append(belief_package)
return belief_packages | python | def _get_belief_package(stmt):
# This list will contain the belief packages for the given statement
belief_packages = []
# Iterate over all the support parents
for st in stmt.supports:
# Recursively get all the belief packages of the parent
parent_packages = _get_belief_package(st)
package_stmt_keys = [pkg.statement_key for pkg in belief_packages]
for package in parent_packages:
# Only add this belief package if it hasn't already been added
if package.statement_key not in package_stmt_keys:
belief_packages.append(package)
# Now make the Statement's own belief package and append it to the list
belief_package = BeliefPackage(stmt.matches_key(), stmt.evidence)
belief_packages.append(belief_package)
return belief_packages | [
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18,879 | sorgerlab/indra | indra/belief/__init__.py | sample_statements | def sample_statements(stmts, seed=None):
"""Return statements sampled according to belief.
Statements are sampled independently according to their
belief scores. For instance, a Staement with a belief
score of 0.7 will end up in the returned Statement list
with probability 0.7.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of INDRA Statements to sample.
seed : Optional[int]
A seed for the random number generator used for sampling.
Returns
-------
new_stmts : list[indra.statements.Statement]
A list of INDRA Statements that were chosen by random sampling
according to their respective belief scores.
"""
if seed:
numpy.random.seed(seed)
new_stmts = []
r = numpy.random.rand(len(stmts))
for i, stmt in enumerate(stmts):
if r[i] < stmt.belief:
new_stmts.append(stmt)
return new_stmts | python | def sample_statements(stmts, seed=None):
if seed:
numpy.random.seed(seed)
new_stmts = []
r = numpy.random.rand(len(stmts))
for i, stmt in enumerate(stmts):
if r[i] < stmt.belief:
new_stmts.append(stmt)
return new_stmts | [
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Statements are sampled independently according to their
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with probability 0.7.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of INDRA Statements to sample.
seed : Optional[int]
A seed for the random number generator used for sampling.
Returns
-------
new_stmts : list[indra.statements.Statement]
A list of INDRA Statements that were chosen by random sampling
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18,880 | sorgerlab/indra | indra/belief/__init__.py | evidence_random_noise_prior | def evidence_random_noise_prior(evidence, type_probs, subtype_probs):
"""Determines the random-noise prior probability for this evidence.
If the evidence corresponds to a subtype, and that subtype has a curated
prior noise probability, use that.
Otherwise, gives the random-noise prior for the overall rule type.
"""
(stype, subtype) = tag_evidence_subtype(evidence)
# Get the subtype, if available
# Return the subtype random noise prior, if available
if subtype_probs is not None:
if stype in subtype_probs:
if subtype in subtype_probs[stype]:
return subtype_probs[stype][subtype]
# Fallback to just returning the overall evidence type random noise prior
return type_probs[stype] | python | def evidence_random_noise_prior(evidence, type_probs, subtype_probs):
(stype, subtype) = tag_evidence_subtype(evidence)
# Get the subtype, if available
# Return the subtype random noise prior, if available
if subtype_probs is not None:
if stype in subtype_probs:
if subtype in subtype_probs[stype]:
return subtype_probs[stype][subtype]
# Fallback to just returning the overall evidence type random noise prior
return type_probs[stype] | [
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18,881 | sorgerlab/indra | indra/belief/__init__.py | tag_evidence_subtype | def tag_evidence_subtype(evidence):
"""Returns the type and subtype of an evidence object as a string,
typically the extraction rule or database from which the statement
was generated.
For biopax, this is just the database name.
Parameters
----------
statement: indra.statements.Evidence
The statement which we wish to subtype
Returns
-------
types: tuple
A tuple with (type, subtype), both strings
Returns (type, None) if the type of statement is not yet handled in
this function.
"""
source_api = evidence.source_api
annotations = evidence.annotations
if source_api == 'biopax':
subtype = annotations.get('source_sub_id')
elif source_api in ('reach', 'eidos'):
if 'found_by' in annotations:
from indra.sources.reach.processor import determine_reach_subtype
if source_api == 'reach':
subtype = determine_reach_subtype(annotations['found_by'])
elif source_api == 'eidos':
subtype = annotations['found_by']
else:
subtype = None
else:
logger.debug('Could not find found_by attribute in reach '
'statement annoations')
subtype = None
elif source_api == 'geneways':
subtype = annotations['actiontype']
else:
subtype = None
return (source_api, subtype) | python | def tag_evidence_subtype(evidence):
source_api = evidence.source_api
annotations = evidence.annotations
if source_api == 'biopax':
subtype = annotations.get('source_sub_id')
elif source_api in ('reach', 'eidos'):
if 'found_by' in annotations:
from indra.sources.reach.processor import determine_reach_subtype
if source_api == 'reach':
subtype = determine_reach_subtype(annotations['found_by'])
elif source_api == 'eidos':
subtype = annotations['found_by']
else:
subtype = None
else:
logger.debug('Could not find found_by attribute in reach '
'statement annoations')
subtype = None
elif source_api == 'geneways':
subtype = annotations['actiontype']
else:
subtype = None
return (source_api, subtype) | [
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Parameters
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statement: indra.statements.Evidence
The statement which we wish to subtype
Returns
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types: tuple
A tuple with (type, subtype), both strings
Returns (type, None) if the type of statement is not yet handled in
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18,882 | sorgerlab/indra | indra/belief/__init__.py | SimpleScorer.score_evidence_list | def score_evidence_list(self, evidences):
"""Return belief score given a list of supporting evidences."""
def _score(evidences):
if not evidences:
return 0
# Collect all unique sources
sources = [ev.source_api for ev in evidences]
uniq_sources = numpy.unique(sources)
# Calculate the systematic error factors given unique sources
syst_factors = {s: self.prior_probs['syst'][s]
for s in uniq_sources}
# Calculate the radom error factors for each source
rand_factors = {k: [] for k in uniq_sources}
for ev in evidences:
rand_factors[ev.source_api].append(
evidence_random_noise_prior(
ev,
self.prior_probs['rand'],
self.subtype_probs))
# The probability of incorrectness is the product of the
# source-specific probabilities
neg_prob_prior = 1
for s in uniq_sources:
neg_prob_prior *= (syst_factors[s] +
numpy.prod(rand_factors[s]))
# Finally, the probability of correctness is one minus incorrect
prob_prior = 1 - neg_prob_prior
return prob_prior
pos_evidence = [ev for ev in evidences if
not ev.epistemics.get('negated')]
neg_evidence = [ev for ev in evidences if
ev.epistemics.get('negated')]
pp = _score(pos_evidence)
np = _score(neg_evidence)
# The basic assumption is that the positive and negative evidence
# can't simultaneously be correct.
# There are two cases to consider. (1) If the positive evidence is
# incorrect then there is no Statement and the belief should be 0,
# irrespective of the negative evidence.
# (2) If the positive evidence is correct and the negative evidence
# is incorrect.
# This amounts to the following formula:
# 0 * (1-pp) + 1 * (pp * (1-np)) which we simplify below
score = pp * (1 - np)
return score | python | def score_evidence_list(self, evidences):
def _score(evidences):
if not evidences:
return 0
# Collect all unique sources
sources = [ev.source_api for ev in evidences]
uniq_sources = numpy.unique(sources)
# Calculate the systematic error factors given unique sources
syst_factors = {s: self.prior_probs['syst'][s]
for s in uniq_sources}
# Calculate the radom error factors for each source
rand_factors = {k: [] for k in uniq_sources}
for ev in evidences:
rand_factors[ev.source_api].append(
evidence_random_noise_prior(
ev,
self.prior_probs['rand'],
self.subtype_probs))
# The probability of incorrectness is the product of the
# source-specific probabilities
neg_prob_prior = 1
for s in uniq_sources:
neg_prob_prior *= (syst_factors[s] +
numpy.prod(rand_factors[s]))
# Finally, the probability of correctness is one minus incorrect
prob_prior = 1 - neg_prob_prior
return prob_prior
pos_evidence = [ev for ev in evidences if
not ev.epistemics.get('negated')]
neg_evidence = [ev for ev in evidences if
ev.epistemics.get('negated')]
pp = _score(pos_evidence)
np = _score(neg_evidence)
# The basic assumption is that the positive and negative evidence
# can't simultaneously be correct.
# There are two cases to consider. (1) If the positive evidence is
# incorrect then there is no Statement and the belief should be 0,
# irrespective of the negative evidence.
# (2) If the positive evidence is correct and the negative evidence
# is incorrect.
# This amounts to the following formula:
# 0 * (1-pp) + 1 * (pp * (1-np)) which we simplify below
score = pp * (1 - np)
return score | [
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18,883 | sorgerlab/indra | indra/belief/__init__.py | SimpleScorer.score_statement | def score_statement(self, st, extra_evidence=None):
"""Computes the prior belief probability for an INDRA Statement.
The Statement is assumed to be de-duplicated. In other words,
the Statement is assumed to have
a list of Evidence objects that supports it. The prior probability of
the Statement is calculated based on the number of Evidences it has
and their sources.
Parameters
----------
st : indra.statements.Statement
An INDRA Statements whose belief scores are to
be calculated.
extra_evidence : list[indra.statements.Evidence]
A list of Evidences that are supporting the Statement (that aren't
already included in the Statement's own evidence list.
Returns
-------
belief_score : float
The computed prior probability for the statement
"""
if extra_evidence is None:
extra_evidence = []
all_evidence = st.evidence + extra_evidence
return self.score_evidence_list(all_evidence) | python | def score_statement(self, st, extra_evidence=None):
if extra_evidence is None:
extra_evidence = []
all_evidence = st.evidence + extra_evidence
return self.score_evidence_list(all_evidence) | [
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Parameters
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An INDRA Statements whose belief scores are to
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extra_evidence : list[indra.statements.Evidence]
A list of Evidences that are supporting the Statement (that aren't
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belief_score : float
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18,884 | sorgerlab/indra | indra/belief/__init__.py | SimpleScorer.check_prior_probs | def check_prior_probs(self, statements):
"""Throw Exception if BeliefEngine parameter is missing.
Make sure the scorer has all the information needed to compute
belief scores of each statement in the provided list, and raises an
exception otherwise.
Parameters
----------
statements : list[indra.statements.Statement]
List of statements to check
"""
sources = set()
for stmt in statements:
sources |= set([ev.source_api for ev in stmt.evidence])
for err_type in ('rand', 'syst'):
for source in sources:
if source not in self.prior_probs[err_type]:
msg = 'BeliefEngine missing probability parameter' + \
' for source: %s' % source
raise Exception(msg) | python | def check_prior_probs(self, statements):
sources = set()
for stmt in statements:
sources |= set([ev.source_api for ev in stmt.evidence])
for err_type in ('rand', 'syst'):
for source in sources:
if source not in self.prior_probs[err_type]:
msg = 'BeliefEngine missing probability parameter' + \
' for source: %s' % source
raise Exception(msg) | [
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statements : list[indra.statements.Statement]
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18,885 | sorgerlab/indra | indra/belief/__init__.py | BayesianScorer.update_probs | def update_probs(self):
"""Update the internal probability values given the counts."""
# We deal with the prior probsfirst
# This is a fixed assumed value for systematic error
syst_error = 0.05
prior_probs = {'syst': {}, 'rand': {}}
for source, (p, n) in self.prior_counts.items():
# Skip if there are no actual counts
if n + p == 0:
continue
prior_probs['syst'][source] = syst_error
prior_probs['rand'][source] = \
1 - min((float(p) / (n + p), 1-syst_error)) - syst_error
# Next we deal with subtype probs based on counts
subtype_probs = {}
for source, entry in self.subtype_counts.items():
for rule, (p, n) in entry.items():
# Skip if there are no actual counts
if n + p == 0:
continue
if source not in subtype_probs:
subtype_probs[source] = {}
subtype_probs[source][rule] = \
1 - min((float(p) / (n + p), 1-syst_error)) - syst_error
# Finally we propagate this into the full probability
# data structures of the parent class
super(BayesianScorer, self).update_probs(prior_probs, subtype_probs) | python | def update_probs(self):
# We deal with the prior probsfirst
# This is a fixed assumed value for systematic error
syst_error = 0.05
prior_probs = {'syst': {}, 'rand': {}}
for source, (p, n) in self.prior_counts.items():
# Skip if there are no actual counts
if n + p == 0:
continue
prior_probs['syst'][source] = syst_error
prior_probs['rand'][source] = \
1 - min((float(p) / (n + p), 1-syst_error)) - syst_error
# Next we deal with subtype probs based on counts
subtype_probs = {}
for source, entry in self.subtype_counts.items():
for rule, (p, n) in entry.items():
# Skip if there are no actual counts
if n + p == 0:
continue
if source not in subtype_probs:
subtype_probs[source] = {}
subtype_probs[source][rule] = \
1 - min((float(p) / (n + p), 1-syst_error)) - syst_error
# Finally we propagate this into the full probability
# data structures of the parent class
super(BayesianScorer, self).update_probs(prior_probs, subtype_probs) | [
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18,886 | sorgerlab/indra | indra/belief/__init__.py | BayesianScorer.update_counts | def update_counts(self, prior_counts, subtype_counts):
"""Update the internal counts based on given new counts.
Parameters
----------
prior_counts : dict
A dictionary of counts of the form [pos, neg] for
each source.
subtype_counts : dict
A dictionary of counts of the form [pos, neg] for
each subtype within a source.
"""
for source, (pos, neg) in prior_counts.items():
if source not in self.prior_counts:
self.prior_counts[source] = [0, 0]
self.prior_counts[source][0] += pos
self.prior_counts[source][1] += neg
for source, subtype_dict in subtype_counts.items():
if source not in self.subtype_counts:
self.subtype_counts[source] = {}
for subtype, (pos, neg) in subtype_dict.items():
if subtype not in self.subtype_counts[source]:
self.subtype_counts[source][subtype] = [0, 0]
self.subtype_counts[source][subtype][0] += pos
self.subtype_counts[source][subtype][1] += neg
self.update_probs() | python | def update_counts(self, prior_counts, subtype_counts):
for source, (pos, neg) in prior_counts.items():
if source not in self.prior_counts:
self.prior_counts[source] = [0, 0]
self.prior_counts[source][0] += pos
self.prior_counts[source][1] += neg
for source, subtype_dict in subtype_counts.items():
if source not in self.subtype_counts:
self.subtype_counts[source] = {}
for subtype, (pos, neg) in subtype_dict.items():
if subtype not in self.subtype_counts[source]:
self.subtype_counts[source][subtype] = [0, 0]
self.subtype_counts[source][subtype][0] += pos
self.subtype_counts[source][subtype][1] += neg
self.update_probs() | [
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18,887 | sorgerlab/indra | indra/belief/__init__.py | BeliefEngine.set_prior_probs | def set_prior_probs(self, statements):
"""Sets the prior belief probabilities for a list of INDRA Statements.
The Statements are assumed to be de-duplicated. In other words,
each Statement in the list passed to this function is assumed to have
a list of Evidence objects that support it. The prior probability of
each Statement is calculated based on the number of Evidences it has
and their sources.
Parameters
----------
statements : list[indra.statements.Statement]
A list of INDRA Statements whose belief scores are to
be calculated. Each Statement object's belief attribute is updated
by this function.
"""
self.scorer.check_prior_probs(statements)
for st in statements:
st.belief = self.scorer.score_statement(st) | python | def set_prior_probs(self, statements):
self.scorer.check_prior_probs(statements)
for st in statements:
st.belief = self.scorer.score_statement(st) | [
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A list of INDRA Statements whose belief scores are to
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18,888 | sorgerlab/indra | indra/belief/__init__.py | BeliefEngine.set_hierarchy_probs | def set_hierarchy_probs(self, statements):
"""Sets hierarchical belief probabilities for INDRA Statements.
The Statements are assumed to be in a hierarchical relation graph with
the supports and supported_by attribute of each Statement object having
been set.
The hierarchical belief probability of each Statement is calculated
based on its prior probability and the probabilities propagated from
Statements supporting it in the hierarchy graph.
Parameters
----------
statements : list[indra.statements.Statement]
A list of INDRA Statements whose belief scores are to
be calculated. Each Statement object's belief attribute is updated
by this function.
"""
def build_hierarchy_graph(stmts):
"""Return a DiGraph based on matches keys and Statement supports"""
g = networkx.DiGraph()
for st1 in stmts:
g.add_node(st1.matches_key(), stmt=st1)
for st2 in st1.supported_by:
g.add_node(st2.matches_key(), stmt=st2)
g.add_edge(st2.matches_key(), st1.matches_key())
return g
def get_ranked_stmts(g):
"""Return a topological sort of statement matches keys from a graph.
"""
node_ranks = networkx.algorithms.dag.topological_sort(g)
node_ranks = reversed(list(node_ranks))
stmts = [g.node[n]['stmt'] for n in node_ranks]
return stmts
def assert_no_cycle(g):
"""If the graph has cycles, throws AssertionError."""
try:
cyc = networkx.algorithms.cycles.find_cycle(g)
except networkx.exception.NetworkXNoCycle:
return
msg = 'Cycle found in hierarchy graph: %s' % cyc
assert False, msg
g = build_hierarchy_graph(statements)
assert_no_cycle(g)
ranked_stmts = get_ranked_stmts(g)
for st in ranked_stmts:
bps = _get_belief_package(st)
supporting_evidences = []
# NOTE: the last belief package in the list is this statement's own
for bp in bps[:-1]:
# Iterate over all the parent evidences and add only
# non-negated ones
for ev in bp.evidences:
if not ev.epistemics.get('negated'):
supporting_evidences.append(ev)
# Now add the Statement's own evidence
# Now score all the evidences
belief = self.scorer.score_statement(st, supporting_evidences)
st.belief = belief | python | def set_hierarchy_probs(self, statements):
def build_hierarchy_graph(stmts):
"""Return a DiGraph based on matches keys and Statement supports"""
g = networkx.DiGraph()
for st1 in stmts:
g.add_node(st1.matches_key(), stmt=st1)
for st2 in st1.supported_by:
g.add_node(st2.matches_key(), stmt=st2)
g.add_edge(st2.matches_key(), st1.matches_key())
return g
def get_ranked_stmts(g):
"""Return a topological sort of statement matches keys from a graph.
"""
node_ranks = networkx.algorithms.dag.topological_sort(g)
node_ranks = reversed(list(node_ranks))
stmts = [g.node[n]['stmt'] for n in node_ranks]
return stmts
def assert_no_cycle(g):
"""If the graph has cycles, throws AssertionError."""
try:
cyc = networkx.algorithms.cycles.find_cycle(g)
except networkx.exception.NetworkXNoCycle:
return
msg = 'Cycle found in hierarchy graph: %s' % cyc
assert False, msg
g = build_hierarchy_graph(statements)
assert_no_cycle(g)
ranked_stmts = get_ranked_stmts(g)
for st in ranked_stmts:
bps = _get_belief_package(st)
supporting_evidences = []
# NOTE: the last belief package in the list is this statement's own
for bp in bps[:-1]:
# Iterate over all the parent evidences and add only
# non-negated ones
for ev in bp.evidences:
if not ev.epistemics.get('negated'):
supporting_evidences.append(ev)
# Now add the Statement's own evidence
# Now score all the evidences
belief = self.scorer.score_statement(st, supporting_evidences)
st.belief = belief | [
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A list of INDRA Statements whose belief scores are to
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18,889 | sorgerlab/indra | indra/belief/__init__.py | BeliefEngine.set_linked_probs | def set_linked_probs(self, linked_statements):
"""Sets the belief probabilities for a list of linked INDRA Statements.
The list of LinkedStatement objects is assumed to come from the
MechanismLinker. The belief probability of the inferred Statement is
assigned the joint probability of its source Statements.
Parameters
----------
linked_statements : list[indra.mechlinker.LinkedStatement]
A list of INDRA LinkedStatements whose belief scores are to
be calculated. The belief attribute of the inferred Statement in
the LinkedStatement object is updated by this function.
"""
for st in linked_statements:
source_probs = [s.belief for s in st.source_stmts]
st.inferred_stmt.belief = numpy.prod(source_probs) | python | def set_linked_probs(self, linked_statements):
for st in linked_statements:
source_probs = [s.belief for s in st.source_stmts]
st.inferred_stmt.belief = numpy.prod(source_probs) | [
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18,890 | sorgerlab/indra | indra/sources/rlimsp/processor.py | RlimspProcessor.extract_statements | def extract_statements(self):
"""Extract the statements from the json."""
for p_info in self._json:
para = RlimspParagraph(p_info, self.doc_id_type)
self.statements.extend(para.get_statements())
return | python | def extract_statements(self):
for p_info in self._json:
para = RlimspParagraph(p_info, self.doc_id_type)
self.statements.extend(para.get_statements())
return | [
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18,891 | sorgerlab/indra | indra/sources/rlimsp/processor.py | RlimspParagraph._get_agent | def _get_agent(self, entity_id):
"""Convert the entity dictionary into an INDRA Agent."""
if entity_id is None:
return None
entity_info = self._entity_dict.get(entity_id)
if entity_info is None:
logger.warning("Entity key did not resolve to entity.")
return None
return get_agent_from_entity_info(entity_info) | python | def _get_agent(self, entity_id):
if entity_id is None:
return None
entity_info = self._entity_dict.get(entity_id)
if entity_info is None:
logger.warning("Entity key did not resolve to entity.")
return None
return get_agent_from_entity_info(entity_info) | [
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18,892 | sorgerlab/indra | indra/sources/rlimsp/processor.py | RlimspParagraph._get_evidence | def _get_evidence(self, trigger_id, args, agent_coords, site_coords):
"""Get the evidence using the info in the trigger entity."""
trigger_info = self._entity_dict[trigger_id]
# Get the sentence index from the trigger word.
s_idx_set = {self._entity_dict[eid]['sentenceIndex']
for eid in args.values()
if 'sentenceIndex' in self._entity_dict[eid]}
if s_idx_set:
i_min = min(s_idx_set)
i_max = max(s_idx_set)
text = '. '.join(self._sentences[i_min:(i_max+1)]) + '.'
s_start = self._sentence_starts[i_min]
annotations = {
'agents': {'coords': [_fix_coords(coords, s_start)
for coords in agent_coords]},
'trigger': {'coords': _fix_coords([trigger_info['charStart'],
trigger_info['charEnd']],
s_start)}
}
else:
logger.info('Unable to get sentence index')
annotations = {}
text = None
if site_coords:
annotations['site'] = {'coords': _fix_coords(site_coords, s_start)}
return Evidence(text_refs=self._text_refs.copy(), text=text,
source_api='rlimsp', pmid=self._text_refs.get('PMID'),
annotations=annotations) | python | def _get_evidence(self, trigger_id, args, agent_coords, site_coords):
trigger_info = self._entity_dict[trigger_id]
# Get the sentence index from the trigger word.
s_idx_set = {self._entity_dict[eid]['sentenceIndex']
for eid in args.values()
if 'sentenceIndex' in self._entity_dict[eid]}
if s_idx_set:
i_min = min(s_idx_set)
i_max = max(s_idx_set)
text = '. '.join(self._sentences[i_min:(i_max+1)]) + '.'
s_start = self._sentence_starts[i_min]
annotations = {
'agents': {'coords': [_fix_coords(coords, s_start)
for coords in agent_coords]},
'trigger': {'coords': _fix_coords([trigger_info['charStart'],
trigger_info['charEnd']],
s_start)}
}
else:
logger.info('Unable to get sentence index')
annotations = {}
text = None
if site_coords:
annotations['site'] = {'coords': _fix_coords(site_coords, s_start)}
return Evidence(text_refs=self._text_refs.copy(), text=text,
source_api='rlimsp', pmid=self._text_refs.get('PMID'),
annotations=annotations) | [
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18,893 | sorgerlab/indra | indra/tools/reading/readers.py | get_reader_classes | def get_reader_classes(parent=Reader):
"""Get all childless the descendants of a parent class, recursively."""
children = parent.__subclasses__()
descendants = children[:]
for child in children:
grandchildren = get_reader_classes(child)
if grandchildren:
descendants.remove(child)
descendants.extend(grandchildren)
return descendants | python | def get_reader_classes(parent=Reader):
children = parent.__subclasses__()
descendants = children[:]
for child in children:
grandchildren = get_reader_classes(child)
if grandchildren:
descendants.remove(child)
descendants.extend(grandchildren)
return descendants | [
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18,894 | sorgerlab/indra | indra/tools/reading/readers.py | get_reader_class | def get_reader_class(reader_name):
"""Get a particular reader class by name."""
for reader_class in get_reader_classes():
if reader_class.name.lower() == reader_name.lower():
return reader_class
else:
logger.error("No such reader: %s" % reader_name)
return None | python | def get_reader_class(reader_name):
for reader_class in get_reader_classes():
if reader_class.name.lower() == reader_name.lower():
return reader_class
else:
logger.error("No such reader: %s" % reader_name)
return None | [
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18,895 | sorgerlab/indra | indra/tools/reading/readers.py | Content.from_file | def from_file(cls, file_path, compressed=False, encoded=False):
"""Create a content object from a file path."""
file_id = '.'.join(path.basename(file_path).split('.')[:-1])
file_format = file_path.split('.')[-1]
content = cls(file_id, file_format, compressed, encoded)
content.file_exists = True
content._location = path.dirname(file_path)
return content | python | def from_file(cls, file_path, compressed=False, encoded=False):
file_id = '.'.join(path.basename(file_path).split('.')[:-1])
file_format = file_path.split('.')[-1]
content = cls(file_id, file_format, compressed, encoded)
content.file_exists = True
content._location = path.dirname(file_path)
return content | [
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18,896 | sorgerlab/indra | indra/tools/reading/readers.py | Content.change_id | def change_id(self, new_id):
"""Change the id of this content."""
self._load_raw_content()
self._id = new_id
self.get_filename(renew=True)
self.get_filepath(renew=True)
return | python | def change_id(self, new_id):
self._load_raw_content()
self._id = new_id
self.get_filename(renew=True)
self.get_filepath(renew=True)
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18,897 | sorgerlab/indra | indra/tools/reading/readers.py | Content.change_format | def change_format(self, new_format):
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the label.
"""
self._load_raw_content()
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return | python | def change_format(self, new_format):
self._load_raw_content()
self._format = new_format
self.get_filename(renew=True)
self.get_filepath(renew=True)
return | [
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18,898 | sorgerlab/indra | indra/tools/reading/readers.py | Content.get_text | def get_text(self):
"""Get the loaded, decompressed, and decoded text of this content."""
self._load_raw_content()
if self._text is None:
assert self._raw_content is not None
ret_cont = self._raw_content
if self.compressed:
ret_cont = zlib.decompress(ret_cont, zlib.MAX_WBITS+16)
if self.encoded:
ret_cont = ret_cont.decode('utf-8')
self._text = ret_cont
assert self._text is not None
return self._text | python | def get_text(self):
self._load_raw_content()
if self._text is None:
assert self._raw_content is not None
ret_cont = self._raw_content
if self.compressed:
ret_cont = zlib.decompress(ret_cont, zlib.MAX_WBITS+16)
if self.encoded:
ret_cont = ret_cont.decode('utf-8')
self._text = ret_cont
assert self._text is not None
return self._text | [
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18,899 | sorgerlab/indra | indra/tools/reading/readers.py | Content.get_filename | def get_filename(self, renew=False):
"""Get the filename of this content.
If the file name doesn't already exist, we created it as {id}.{format}.
"""
if self._fname is None or renew:
self._fname = '%s.%s' % (self._id, self._format)
return self._fname | python | def get_filename(self, renew=False):
if self._fname is None or renew:
self._fname = '%s.%s' % (self._id, self._format)
return self._fname | [
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