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Medicine

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

Maciej Sypetkowski, Joanna Krawczyk, Łukasz Smoliński, Remigiusz Kinas, Przemysław Pietrzak, Tomasz Jetka, Rafał Powalski

Featured May 23, 2026

Original Paper

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Simply

OmicsLM helps computers understand biology by turning complex gene data into a special code that language models can read, letting them answer tough questions about many samples at once.

In depth
The paper introduces OmicsLM, a multimodal large language model that directly integrates quantitative omics profiles into an LLM's context. This is achieved by projecting high-dimensional omics data into a compact continuous vector, which replaces a dedicated placeholder token. Additionally, the model's vocabulary is augmented with explicit gene tokens, enabling precise language-guided reasoning and comparative analysis across multiple biological samples within a single prompt.

Key Takeaways

  • 1
    OmicsLM introduces a novel architecture that aligns continuous omics embeddings with the text space of Large Language Models, allowing direct processing of quantitative biological data.
  • 2
    The model augments the LLM's vocabulary with explicit gene tokenization, preventing fragmentation of biological nomenclature and improving gene-level reasoning.
  • 3
    A new benchmark, GEO-OmicsQA, is introduced for multi-sample biological question answering, demonstrating OmicsLM's superior performance in language-guided reasoning over real expression profiles.

Conceptual Flow

HIGH LEVEL
1
Methodology: How OmicsLM Connects Data and Language

The system takes gene data, turns it into a special computer code, and feeds it to a smart language program to understand and answer questions.

Gene Data
Convert to Code
Smart Language Program
2
Results: OmicsLM's Enhanced Biological Reasoning

The new system is much better at answering complex biology questions using real gene data compared to older methods or general smart language programs.

Old Methods
General Programs
Compare Performance
OmicsLM: Better Answers