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Genetics

OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning

Maciej Sypetkowski, Joanna Krawczyk, Łukasz Smoliński, Remigiusz Kinas, Przemysław Pietrzak

Featured May 31, 2026

Original Paper

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Simply

A new AI model called OmicsLM helps computers understand biology better by turning complex gene data into simple 'omics tokens' that can be mixed with regular questions, letting it compare many samples at once.

In depth
The paper introduces OmicsLM, a novel multimodal large language model that directly integrates quantitative omics profiles into the text-centric input space of LLMs. Instead of serializing omics data as gene-name sequences, OmicsLM represents each transcriptomic profile as a compact continuous vector via a linear projection, allowing it to be treated as a distinct token. This approach preserves quantitative signal, enables efficient multi-sample comparative reasoning, and is augmented by explicit gene tokenization to improve biological nomenclature understanding.

Key Takeaways

  • 1
    OmicsLM integrates high-dimensional omics data into LLMs by projecting transcriptomic profiles into continuous vector embeddings, preserving quantitative signal.
  • 2
    The model supports multi-sample reasoning by allowing multiple projected omics embeddings to be interleaved with natural language instructions within a single context window.
  • 3
    A novel gene-aware vocabulary augmentation strategy improves the LLM's ability to process and generate biological nomenclature, such as gene symbols, as atomic tokens.

Conceptual Flow

HIGH LEVEL
1
Methodology: How OmicsLM Connects Biology and Language

The model takes complex gene data, turns it into a special computer 'word,' and then mixes it with regular questions so the computer can understand and talk about biology.

Gene Data
Text Question
Combine & Understand
Biological Answer
2
Results: Better Biological Reasoning

This new way helps the computer answer tricky biology questions much better than old methods, especially when comparing many gene samples at once.

Old Way: Text Only
Old Way: Gene Data Only
New Way: OmicsLM
Compare Performance
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