upload hubscripts/pubtator_central_hub.py to hub from bigbio repo

pubtator_central.py

@@ -0,0 +1,305 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+PubTator Central (PTC, https://www.ncbi.nlm.nih.gov/research/pubtator/) [1] is a web service for
+exploring and retrieving bioconcept annotations in full text biomedical articles. PTC provides
+automated annotations from state-of-the-art text mining systems for genes/proteins, genetic
+variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC
+annotates PubMed (30 million abstracts), the PMC Open Access Subset and the Author Manuscript
+Collection (3 million full text articles). Updated entity identification methods and a
+disambiguation module [2] based on cutting-edge deep learning techniques provide increased accuracy.
+This FTP repository aggregated all the bio-entity annotations in PTC in tab-separated text format.
+The files are expected to be updated monthly.
+
+REFERENCE:
+---------------------------------------------------------------------------
+[1] Wei C-H, Allot A, Leaman R and Lu Z (2019) "PubTator Central: Automated Concept Annotation for
+    Biomedical Full Text Articles", Nucleic Acids Res.
+[2] wei C-H, et al., (2019) "Biomedical Mention Disambiguation Using a Deep Learning Approach",
+    ACM-BCB 2019, September 7-10, 2019, Niagara Falls, NY, USA.
+[3] Wei C-H, Kao H-Y, Lu Z (2015) "GNormPlus: An Integrative Approach for Tagging Gene, Gene Family
+    and Protein Domain", 2015, Article ID 918710
+[4] Leaman R and Lu Z (2013) "TaggerOne: joint named entity recognition and normalization with
+    semi-Markov Models", Bioinformatics, 32(18): 839-2846
+[5] Wei C-H, Kao H-Y, Lu Z (2012) "SR4GN: a species recognition software tool for gene normalization",
+    PLoS ONE,7(6):e38460
+[6] Wei C-H, et al., (2017) "Integrating genomic variant information from literature with dbSNP and
+    ClinVar for precision medicine", Bioinformatics,34(1): 80-87
+"""
+
+
+from typing import Dict, Iterator, List, Tuple
+
+import datasets
+from bioc import pubtator
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{10.1093/nar/gkz389,
+  title        = {{PubTator central: automated concept annotation for biomedical full text articles}},
+  author       = {Wei, Chih-Hsuan and Allot, Alexis and Leaman, Robert and Lu, Zhiyong},
+  year         = 2019,
+  month        = {05},
+  journal      = {Nucleic Acids Research},
+  volume       = 47,
+  number       = {W1},
+  pages        = {W587-W593},
+  doi          = {10.1093/nar/gkz389},
+  issn         = {0305-1048},
+  url          = {https://doi.org/10.1093/nar/gkz389},
+  eprint       = {https://academic.oup.com/nar/article-pdf/47/W1/W587/28880193/gkz389.pdf}
+}
+"""
+
+_DATASETNAME = "pubtator_central"
+_DISPLAYNAME = "PubTator Central"
+
+_DESCRIPTION = """\
+PubTator Central (PTC, https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for
+exploring and retrieving bioconcept annotations in full text biomedical articles. PTC provides
+automated annotations from state-of-the-art text mining systems for genes/proteins, genetic
+variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC
+annotates PubMed (30 million abstracts), the PMC Open Access Subset and the Author Manuscript
+Collection (3 million full text articles). Updated entity identification methods and a
+disambiguation module based on cutting-edge deep learning techniques provide increased accuracy.
+"""
+
+_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/research/pubtator/"
+
+_LICENSE = 'National Center fr Biotechnology Information PUBLIC DOMAIN NOTICE'
+
+_URLS = {
+    "sample": "https://ftp.ncbi.nlm.nih.gov/pub/lu/PubTatorCentral/bioconcepts2pubtatorcentral.offset.sample",
+    "full": "https://ftp.ncbi.nlm.nih.gov/pub/lu/PubTatorCentral/bioconcepts2pubtatorcentral.offset.gz",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
+
+_SOURCE_VERSION = "2022.01.08"
+_BIGBIO_VERSION = "1.0.0"
+
+# Maps the entity types in PubTator to the name of the database they are grounded to
+_TYPE_TO_DB_NAME = {
+    "Gene": "ncbi_gene",
+    "Disease": "mesh",
+    "Species": "ncbi_taxon",
+    "Chemical": "mesh",
+    "CellLine": "cellosaurus",
+}
+
+_DB_NAME_TO_URL = {
+    "ncbi_gene": "https://www.ncbi.nlm.nih.gov/gene/",
+    "mesh": "https://www.nlm.nih.gov/mesh/meshhome.html",
+    "ncbi_taxon": "https://www.ncbi.nlm.nih.gov/taxonomy/",
+    "cellosaurus": "https://web.expasy.org/cellosaurus/",
+    "ncbi_dbsnp": "https://www.ncbi.nlm.nih.gov/snp/",
+    "tmvar": "https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/",
+}
+
+
+class PubtatorCentralDataset(datasets.GeneratorBasedBuilder):
+    """PubTator Central"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        # sample source
+        BigBioConfig(
+            name="pubtator_central_sample_source",
+            version=SOURCE_VERSION,
+            description="PubTator Central sample source schema",
+            schema="source",
+            subset_id="pubtator_central_sample",
+        ),
+        # sample big bio
+        BigBioConfig(
+            name="pubtator_central_sample_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="PubTator Central sample BigBio schema",
+            schema="bigbio_kb",
+            subset_id="pubtator_central_sample",
+        ),
+        # full dataset source
+        BigBioConfig(
+            name="pubtator_central_source",
+            version=SOURCE_VERSION,
+            description="PubTator Central source schema",
+            schema="source",
+            subset_id="pubtator_central",
+        ),
+        # full dataset bigbio
+        BigBioConfig(
+            name="pubtator_central_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="PubTator Central BigBio schema",
+            schema="bigbio_kb",
+            subset_id="pubtator_central",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "pubtator_central_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "pmid": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "abstract": datasets.Value("string"),
+                    "mentions": [
+                        {
+                            "concept_id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "offsets": datasets.Sequence(datasets.Value("int32")),
+                        }
+                    ],
+                }
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = (
+            _URLS["sample"]
+            if self.config.subset_id.endswith("sample")
+            else _URLS["full"]
+        )
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepath: str, split: str
+    ) -> Iterator[Tuple[str, Dict]]:
+        if self.config.schema == "source":
+            for source_example in self._pubtator_to_source(filepath):
+                yield source_example["pmid"], source_example
+
+        elif self.config.schema == "bigbio_kb":
+            for kb_example in self._pubtator_to_bigbio_kb(filepath):
+                yield kb_example["id"], kb_example
+
+    @staticmethod
+    def _pubtator_to_source(filepath: Dict) -> Iterator[Dict]:
+        with open(filepath, "r") as f:
+            for doc in pubtator.iterparse(f):
+                source_example = {
+                    "pmid": doc.pmid,
+                    "title": doc.title,
+                    "abstract": doc.abstract,
+                    "mentions": [
+                        {
+                            "concept_id": mention.id,
+                            "type": mention.type,
+                            "text": mention.text,
+                            "offsets": [mention.start, mention.end],
+                        }
+                        for mention in doc.annotations
+                    ],
+                }
+                yield source_example
+
+    def _pubtator_to_bigbio_kb(self, filepath: Dict) -> Iterator[Dict]:
+        with open(filepath, "r") as f:
+            unified_example = {}
+            for doc in pubtator.iterparse(f):
+                unified_example["id"] = doc.pmid
+                unified_example["document_id"] = doc.pmid
+
+                unified_example["passages"] = [
+                    {
+                        "id": doc.pmid + "_title",
+                        "type": "title",
+                        "text": [doc.title],
+                        "offsets": [[0, len(doc.title)]],
+                    },
+                    {
+                        "id": doc.pmid + "_abstract",
+                        "type": "abstract",
+                        "text": [doc.abstract],
+                        "offsets": [
+                            [
+                                # +1 assumes the title and abstract will be joined by a space.
+                                len(doc.title) + 1,
+                                len(doc.title) + 1 + len(doc.abstract),
+                            ]
+                        ],
+                    },
+                ]
+
+                unified_entities = []
+                for i, entity in enumerate(doc.annotations):
+                    # We need a unique identifier for this entity, so build it from the document id and entity id
+                    unified_entity_id = "_".join([doc.pmid, entity.id, str(i)])
+                    # Determining db_name is tricky so use a helper to determine this from the entity annotation
+                    db_name = self._get_db_name(entity)
+                    unified_entities.append(
+                        {
+                            "id": unified_entity_id,
+                            "type": entity.type,
+                            "text": [entity.text],
+                            "offsets": [[entity.start, entity.end]],
+                            "normalized": [{"db_name": db_name, "db_id": entity.id}],
+                        }
+                    )
+
+                unified_example["entities"] = unified_entities
+                unified_example["relations"] = []
+                unified_example["events"] = []
+                unified_example["coreferences"] = []
+
+                yield unified_example
+
+    @staticmethod
+    def _get_db_name(entity: pubtator.PubTatorAnn) -> str:
+        if entity.type in _TYPE_TO_DB_NAME:
+            db_name = _TYPE_TO_DB_NAME[entity.type]
+        elif entity.type in ["Mutation", "ProteinMutation", "DNAMutation"]:
+            # Mutation anntotations are grounded to either tmVar or dbSNP
+            if entity.id.startswith("tmVar"):
+                db_name = "tmVar"
+            else:
+                db_name = "ncbi_dbsnp"
+        else:
+            db_name = "unknown"
+        return db_name