Datasets:
license: mit
task_categories:
- feature-extraction
tags:
- biology
- genomics
- proteins
- multimodal
- mimic
pretty_name: LORE Examples
thumbnail: >-
https://raw.githubusercontent.com/PolymathicAI/MIMIC/main/assets/MIMIC_logo.png
configs:
- config_name: raw
data_files: raw/examples.jsonl
default: true
- config_name: tokenized
data_files: tokenized/examples.jsonl
default_config_name: raw
LORE Examples
A small set of matched multimodal examples from LORE, for the MIMIC model — enough to try inference, embedding, and generation across DNA, RNA, and protein modalities without wiring up your own data.
Each example is a single biological entity (a transcript and/or its protein) with several co-observed modalities. Rows are drawn from the held-out (validation) split of MIMIC's training data, so they are in-distribution and length-bounded to the model's context window.
Contents
Two parallel views of the same rows, exposed as two dataset configs:
| Config | Format | Use |
|---|---|---|
raw (raw/examples.jsonl) |
modality name → raw value (nucleotide/AA string, per-position track, …) | model.input([row]) — the tokenizers run for you |
tokenized (tokenized/examples.jsonl) |
tok_<modality> → token-id list |
model.input([row]) — pre-tokenized, so the heavy tokenizers (BioBERT text, ESM3 structure) don't run |
raw is the default config, so load_dataset("polymathic-ai/LORE-examples")
(no config name) loads it. Every row carries kind (rna / protein / both)
plus uniprot_id / genome_feature_id anchors. Both views cover the same rows
and the same modalities; tokenized just skips running the tokenizers (and the
one-time ESM3 weight download for prot_struct).
Usage
from datasets import load_dataset
from mimic import load_pretrained
model = load_pretrained(version="1.0")
ANCHORS = ("kind", "uniprot_id", "genome_feature_id")
def to_sample(row):
# drop anchor columns and modalities absent from this row (stored as null)
return {k: v for k, v in row.items() if k not in ANCHORS and v is not None}
# raw view — modality name -> raw value; tokenizers run inside input()
raw = load_dataset("polymathic-ai/LORE-examples", "raw", split="train")
model.input([to_sample(raw[0])])
reps = model.embed() # {"full": [B, N, D], "mod_ids": [B, N]}
# tokenized view — tok_ keys with pre-tokenized ids
# (skips running the BioBERT/ESM3 tokenizers; same rows as the raw view)
tok = load_dataset("polymathic-ai/LORE-examples", "tokenized", split="train")
model.input([to_sample(tok[0])])
Modalities
MIMIC represents each molecule as a set of co-observed modalities grouped into
three tracks: nucleic (RNA/DNA and its per-position annotations), protein
(amino-acid sequence, structure, and derived features), and text (free-text /
categorical context). Each row here populates a subset of these under its short name
(raw view) or its tok_ key (tokenized view). The authoritative per-checkpoint
list is model.modality_info. The Dtype column is the raw/decoded Python type; the tokenized config stores every modality as list[int]. A few assay tracks (atac, cage, rasp2,
prot_abund) are context-conditional: pass a free-text context alongside
them to condition on cell-state / assay metadata — the Conditioning context
column shows a real example for each.
| Modality | Track | Dtype | Description | Example | Conditioning context (context) |
|---|---|---|---|---|---|
rna_seq |
nucleic | str |
RNA/DNA nucleotide sequence (unspliced) — the core nucleic input | "UUUGGAAACUUU…" |
— |
cds_junctions |
nucleic | str |
Coding-sequence (CDS) exon–exon junction positions, per position | "…0001000…" |
— |
splice_regions |
nucleic | str |
Splice-region (exon) annotation, per position | "…0011100…" |
— |
splice_jctns_5cls |
nucleic | str |
Per-position 5-class splice-site type: 0=none, 1=acceptor, 2=donor, 3=TSS (first-exon start), 4=TES (last-exon end) |
"…00020…0100…" |
— |
is_coding |
nucleic | list[int] |
Coding vs. non-coding flag | [1] |
— |
feature_type |
nucleic | list[str] |
Genomic feature-type label | ['protein_coding'] |
— |
phylop_human |
nucleic | list[float] |
phyloP evolutionary-conservation score (human), per position | [-0.66, 1.04, …] |
— |
phylop_mouse |
nucleic | list[float] |
phyloP evolutionary-conservation score (mouse), per position | [-0.26, -0.92, …] |
— |
atac |
nucleic | str |
ATAC-seq chromatin-accessibility signal, per position (N = unmeasured). Cell-state-conditional |
"…N888887…" |
"human, GM23338 lymphoblastoid cell line (EBV-transformed B lymphocyte)" |
cage |
nucleic | list[float] |
CAGE transcription-start signal, per position. Cell-state-conditional | [0.001, 0.001, …] |
"skeletal muscle, human, fetal" |
rasp2 |
nucleic | list[float] |
RASP2 (icSHAPE-style) RNA-structure reactivity, per position (nan where unmeasured). Condition-conditional |
[nan, 0.42, …] |
"technology: icSHAPE, reagent: NAI-N3, in vivo, cell line: K562, human" |
aa_seq |
protein | str |
Amino-acid (protein) sequence — the core protein input | "MTPPERLFLP…" |
— |
rna_codons |
protein | list[str] |
Codon sequence aligned to the protein (nucleotide content, protein-aligned track) | ['AUG', 'ACA', 'CCA', …] |
— |
prot_struct |
protein | biotite AtomArray |
Protein 3D structure as ESM3 VQVAE tokens (decode to a backbone via detokenize_structure) |
AtomArray (backbone) |
— |
dssp |
protein | str |
DSSP secondary-structure class, per residue | "CCXX…HHH…" |
— |
sasa |
protein | list[float] |
Solvent-accessible surface area, per residue | [225.1, 128.6, …] |
— |
prot_abund |
protein | list[float] |
Protein abundance (PaxDb ppm), scalar. Cell-state-conditional | [385.6] |
"Leptospira interrogans (bacterium), control" |
funcprot_caption |
protein | str |
Free-text protein functional caption | "Catalyzes the hydrolysis of…" |
— |
masif_charge |
protein | list[float] |
MaSIF surface Poisson–Boltzmann charge, per vertex | [6.9, -3.3, …] |
— |
masif_hbond |
protein | list[float] |
MaSIF surface hydrogen-bond potential, per vertex | [-1.77, -1.64, …] |
— |
masif_hydrophobicity |
protein | list[float] |
MaSIF surface hydrophobicity, per vertex | [0.32, -0.31, …] |
— |
masif_si_index |
protein | list[float] |
MaSIF surface shape-index, per vertex | [0.34, 0.22, …] |
— |
masif_n_vertices |
protein | list[float] |
MaSIF surface vertex count, per patch | [65.0, 42.0, …] |
— |
context |
text | str |
Free-text semantic context (e.g. cell-state / assay) — the conditioning channel itself | "HepG2 cell line" |
— |
corpus |
text | str |
Free-text biomedical literature (PubMed abstracts / articles), used as a language corpus | "…regulates cell-cycle arrest and apoptosis…" |
— |
gene_family_txt |
text | str |
Organism taxonomic lineage as free text (broad clade → phylum → class → order → family → genus → species) | "metazoa chordata mammalia primates hominidae homo homo sapiens" |
— |
See also
- Model:
polymathic-ai/MIMIC— the model these examples are for. - Code:
PolymathicAI/MIMIC(pip install git+https://github.com/PolymathicAI/MIMIC.git).
Provenance & license
Derived from the validation split of MIMIC's training corpus (LORE). Released under the MIT license, matching the model code. See the MIMIC repository for details and citation.