Automating Exploratory Multiomics Research via Language Models

Shang Qu; Ning Ding; Linhai Xie; Yifei Li; Zaoqu Liu; Kaiyan Zhang; Yibai Xiong; Yuxin Zuo; Zhangren Chen; Ermo Hua; Xingtai Lv; Youbang Sun; Yang Li; Dong Li; Fuchu He; Bowen Zhou

arXiv · 2506.07591

Automating Exploratory Multiomics Research via Language Models

Shang Qu·Ning Ding·Linhai Xie·Yifei Li·Zaoqu Liu·Kaiyan Zhang·Yibai Xiong·Yuxin Zuo·Zhangren Chen·Ermo Hua·Xingtai Lv·Youbang Sun·Yang Li·Dong Li·Fuchu He·Bowen Zhou

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Abstract

This paper introduces PROTEUS, a fully automated system that produces data-driven hypotheses from raw data files. We apply PROTEUS to clinical proteogenomics, a field where effective downstream data analysis and hypothesis proposal is crucial for producing novel discoveries. PROTEUS uses separate modules to simulate different stages of the scientific process, from open-ended data exploration to specific statistical analysis and hypothesis proposal. It formulates research directions, tools, and results in terms of relationships between biological entities, using unified graph structures to manage complex research processes. We applied PROTEUS to 10 clinical multiomics datasets from published research, arriving at 360 total hypotheses. Results were evaluated through external data validation and automatic open-ended scoring. Through exploratory and iterative research, the system can navigate high-throughput and heterogeneous multiomics data to arrive at hypotheses that balance reliability and novelty. In addition to accelerating multiomic analysis, PROTEUS represents a path towards tailoring general autonomous systems to specialized scientific domains to achieve open-ended hypothesis generation from data.

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Shang Qu, Ning Ding, Linhai Xie, Yifei Li, Zaoqu Liu, Kaiyan Zhang, Yibai Xiong, Yuxin Zuo, Zhangren Chen, Ermo Hua, Xingtai Lv, Youbang Sun, Yang Li, Dong Li, Fuchu He, Bowen Zhou. 2025-06-09. Automating Exploratory Multiomics Research via Language Models. https://arxiv.org/abs/2506.07591

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