OmicsLM：一种多模态大语言模型用于多样本组学推理
OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning
AI总结：
OmicsLM通过整合定量组学数据与自然语言任务，实现多样本组学推理，其在多任务类型上表现优异，尤其在生物语言指导的推理任务中超越专用模型和通用LLM。
摘要：
解读转录组数据是现代生物学中最常见的分析任务之一。然而，目前大多数模型要么无法生成自然语言的生物学解释，要么无法直接访问定量组学测量数据。我们介绍了OmicsLM，一种多模态大语言模型，能够将定量组学资料与自然语言生物学任务连接起来。OmicsLM将每个转录组资料表示为LLM上下文中的紧凑连续表示。这种接口保留了定量表达信号，同时允许自然语言指令、显式基因提及和多个交错的生物样本在同一模型上下文中处理。我们训练OmicsLM超过550万个遵循指令的示例，涵盖70多种任务类型，结合连续的转录组输入、通过多样语言模板渲染的实验数据以及自由文本的生物知识和问答数据。这种混合涵盖了细胞类型注释、扰动预测、临床预测、通路推理和开放式的生物问答。现有基准测试评估的是单个资料预测或纯文本的生物问答，无法衡量语言引导的多样本推理。为此，我们引入了GEO-OmicsQA，一个基于真实Gene Expression Omnibus（GEO）研究构建的多样本生物问答基准。我们证明OmicsLM可以直接使用表达资料，在单个资料任务上与专用组学模型相当，但在语言引导的生物推理上超越了专用组学模型和通用LLM。
英文摘要：
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.
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