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@@ -83,8 +83,8 @@ print(out["splice_jctns_5cls"]["tokens"].shape) # (num_positions,) predicted
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  `load_pretrained` fetches only `config.json` + `model.safetensors`; tokenizers ship
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  inside the `mimic` package.
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- > **Install:** `pip install git+https://github.com/PolymathicAI/MIMIC.git` (imports as `mimic`).
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- > Source and docs: [github.com/PolymathicAI/MIMIC](https://github.com/PolymathicAI/MIMIC).
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  ## Files
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@@ -119,34 +119,7 @@ few assay tracks (`atac`, `cage`, `rasp2`, `prot_abund`) are **context-condition
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  pass a free-text `context` alongside them to condition on cell-state / assay
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  metadata — the `Conditioning context` column shows a real example for each.
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- | Modality | Track | Description | Example | Conditioning context (`tok_context`) |
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- |---|---|---|---|---|
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- | `rna_seq` | nucleic | RNA/DNA nucleotide sequence (unspliced) — the core nucleic input | `"UUUGGAAACUUU…"` | — |
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- | `cds_junctions` | nucleic | Coding-sequence (CDS) exon–exon junction positions, per position | `"…0001000…"` | — |
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- | `splice_regions` | nucleic | Splice-region (exon) annotation, per position | `"…0011100…"` | — |
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- | `splice_jctns_5cls` | nucleic | 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…"` | — |
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- | `is_coding` | nucleic | Coding vs. non-coding flag | `[1]` | — |
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- | `feature_type` | nucleic | Genomic feature-type label | `['protein_coding']` | — |
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- | `phylop_human` | nucleic | phyloP evolutionary-conservation score (human), per position | `[-0.66, 1.04, …]` | — |
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- | `phylop_mouse` | nucleic | phyloP evolutionary-conservation score (mouse), per position | `[-0.26, -0.92, …]` | — |
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- | `atac` | nucleic | ATAC-seq chromatin-accessibility signal, per position (`N` = unmeasured). **Cell-state-conditional** | `"…NNNN…"` | `"human, GM23338 lymphoblastoid cell line (EBV-transformed B lymphocyte)"` |
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- | `cage` | nucleic | CAGE transcription-start signal, per position. **Cell-state-conditional** | `[0.001, 0.001, …]` | `"skeletal muscle, human, fetal"` |
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- | `rasp2` | nucleic | 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"` |
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- | `aa_seq` | protein | Amino-acid (protein) sequence — the core protein input | `"MTPPERLFLP…"` | — |
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- | `rna_codons` | protein | Codon sequence aligned to the protein (nucleotide content, protein-aligned track) | `['AUG', 'ACA', 'CCA', …]` | — |
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- | `prot_struct` | protein | Protein 3D structure as ESM3 VQVAE tokens (decode to a backbone via `detokenize_structure`) | `[754, 1510, 49, …]` | — |
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- | `dssp` | protein | DSSP secondary-structure class, per residue | `"CCXX…HHH…"` | — |
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- | `sasa` | protein | Solvent-accessible surface area, per residue | `[225.1, 128.6, …]` | — |
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- | `prot_abund` | protein | Protein abundance (PaxDb ppm), scalar. **Cell-state-conditional** | `[385.6]` | `"Leptospira interrogans (bacterium), control"` |
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- | `funcprot_caption` | protein | Free-text protein functional caption | `"Catalyzes the hydrolysis of…"` | — |
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- | `masif_charge` | protein | MaSIF surface Poisson–Boltzmann charge, per vertex | `[6.9, -3.3, …]` | — |
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- | `masif_hbond` | protein | MaSIF surface hydrogen-bond potential, per vertex | `[-1.77, -1.64, …]` | — |
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- | `masif_hydrophobicity` | protein | MaSIF surface hydrophobicity, per vertex | `[0.32, -0.31, …]` | — |
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- | `masif_si_index` | protein | MaSIF surface shape-index, per vertex | `[0.34, 0.22, …]` | — |
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- | `masif_n_vertices` | protein | MaSIF surface vertex count, per patch | `[65.0, 42.0, …]` | — |
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- | `context` | text | Free-text semantic context (e.g. cell-state / assay) — the conditioning channel itself | `"HepG2 cell line"` | — |
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- | `corpus` | text | Free-text corpus / source label | `"UniProtKB/Swiss-Prot"` | — |
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- | `gene_family_txt` | text | Free-text gene-family description | `"Zinc-finger C2H2 family"` | — |
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  <!-- Training data section — drafted, held back for now. Restore (uncomment) when ready.
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  `load_pretrained` fetches only `config.json` + `model.safetensors`; tokenizers ship
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  inside the `mimic` package.
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+ **Install:** `pip install git+https://github.com/PolymathicAI/MIMIC.git` (imports as `mimic`).
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+ Source and docs: [github.com/PolymathicAI/MIMIC](https://github.com/PolymathicAI/MIMIC).
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  ## Files
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  pass a free-text `context` alongside them to condition on cell-state / assay
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  metadata — the `Conditioning context` column shows a real example for each.
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+ See the full modality table (with per-modality examples and conditioning contexts) on the [LORE-examples dataset card](https://huggingface.co/datasets/polymathic-ai/LORE-examples).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  <!-- Training data section — drafted, held back for now. Restore (uncomment) when ready.
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