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.
View on arXiv@article{qu2025_2506.07591,
title={ Automating Exploratory Multiomics Research via Language Models },
author={ Shang Qu and Ning Ding and Linhai Xie and Yifei Li and Zaoqu Liu and Kaiyan Zhang and Yibai Xiong and Yuxin Zuo and Zhangren Chen and Ermo Hua and Xingtai Lv and Youbang Sun and Yang Li and Dong Li and Fuchu He and Bowen Zhou },
journal={arXiv preprint arXiv:2506.07591},
year={ 2025 }
}