arXiv cs.AI· Maciej Sypetkowski, {\L}ukasz Smoli{\'n}ski, Remigiusz Kinas, Joanna Krawczyk, Bart{\l}omiej Hofman, Aleksandra Mo{\.z}wi{\l}{\l}o, Micha{\l} Pikusa, Tomasz Jetka, Rafa{\l} Powalski·· 14 小时前AI 评分35
OmicsLM：面向多样本组学推理的多模态大语言模型
OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning
AI 导读
OmicsLM 是一个 9B 多模态语言模型，将每个 bulk 或单细胞转录组表示为紧凑的连续嵌入向量，可与自然语言指令及同上下文中的其他样本交错输入。其指令微调语料覆盖 157 个生物学与通用任务组，训练包含 200 亿 token 持续预训练和 521 万轮对话监督微调。
正文 · 原文
Abstract:Large language models can reason over biological knowledge, but they are not naturally equipped to work with quantitative omics measurements. Existing approaches either convert expression profiles into long textual representations, often losing quantitative information, or connect omics encoders to language models for narrow tasks. We introduce OmicsLM, a 9B multimodal language model that represents each bulk or single-cell transcriptome as a compact continuous embedding that can be interleaved with natural-language instructions and with other samples in the same context. An key contribution is a training corpus that connects experimental measurements with biological knowledge and task supervision. We combine templates linking transcriptomic profiles to annotations and experimental outcomes, reasoning examples connecting evidence across sources, and LLM-generated supervision with task-specific quality checks. The instruction-tuning corpus spans 157 biological and general-purpose task groups. Training includes continued pretraining on 20 billion tokens and supervised fine-tuning on 5.21 million conversations. We further introduce GEO-OmicsQA, a publication-disjoint benchmark of 3,000 questions requiring models to interpret and compare expression profiles from real GEO studies. OmicsLM achieves the best results among the evaluated models on three of four held-out annotation tasks and on all six GEO-OmicsQA variants, and the best average performance among compared methods on PerturbQA perturbation-response prediction. Ablations show a clear advantage for continuous omics representations over a text-only gene-list representation. Together, these results show that quantitative transcriptomic measurements and biological language can be integrated within a general conversational model, enabling both predictive tasks and language-guided interpretation of experimental samples.
| Comments: | 47 pages (10 pages main text, 29 pages appendix), 1 figure, 16 tables |
| Subjects: | Genomics (q-bio.GN); Artificial Intelligence (cs.AI); Cell Behavior (q-bio.CB) |
| Cite as: | arXiv:2605.06728 [q-bio.GN] |
| (or arXiv:2605.06728v2 [q-bio.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06728 arXiv-issued DOI via DataCite |
Submission history
From: Tomasz Jetka Dr. [view email]
[v1]
Thu, 7 May 2026 11:27:11 UTC (250 KB)
[v2]
Sun, 25 Oct 2026 08:32:57 UTC (241 KB)
来源：arXiv cs.AI · arxiv.org