polymathic-ai/LORE-examples

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.