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

TL;DR

OmicsLM is a multimodal large language model that integrates quantitative transcriptomic data with natural language processing to enhance biological reasoning and question answering across multiple samples.

Contribution

It introduces a novel multimodal LLM that combines omics data with natural language, trained on extensive instruction-following examples for diverse biological tasks.

Findings

OmicsLM performs comparably to specialized models on profile-level tasks.

It outperforms both specialized omics models and general LLMs on language-guided biological reasoning.

The model enables direct use of expression profiles for complex biological question answering.

Abstract

Interpreting transcriptomic data is one of the most common analytical tasks in modern biology. Yet most current models either consume expression profiles without producing natural-language biological explanations, or reason in language without direct access to quantitative omics measurements. We introduce OmicsLM, a multimodal LLM that connects quantitative omics profiles with natural-language biological tasks. OmicsLM represents each transcriptomic profile as a compact continuous representation within the LLM context. This interface preserves quantitative expression signal while allowing natural-language instructions, explicit gene mentions, and multiple interleaved biological samples to be processed together in one model context. We train OmicsLM on more than 5.5 million instruction-following examples spanning over 70 task types, combining continuous transcriptomic inputs, experimental data rendered through diverse language templates, and free-text biological knowledge and question-answering data. This mixture covers cell type annotation, perturbation prediction, clinical prediction, pathway reasoning, and open-ended biological question answering. Existing benchmarks evaluate either profile-level prediction or text-only biological QA, leaving language-guided, multi-sample reasoning over real expression profiles unmeasured. To close this gap, we introduce GEO-OmicsQA, a benchmark for multi-sample biological question answering built from real Gene Expression Omnibus (GEO) studies. We demonstrate that OmicsLM can use expression profiles directly and perform comparably to specialized omics models on profile-level tasks, while outperforming both omics-specialized models and general LLMs on language-guided biological reasoning over expression data.