README.md

---
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
---

<p align="center">
  <img src="MIMIC_logo.png" alt="MIMIC" width="320">
</p>

# LORE Examples

A small set of **matched multimodal examples** from LORE, for the
[MIMIC](https://github.com/PolymathicAI/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

```python
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`](https://huggingface.co/polymathic-ai/MIMIC) — the model these examples are for.
- **Code:** [`PolymathicAI/MIMIC`](https://github.com/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](https://github.com/PolymathicAI/MIMIC) for details and citation.