OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine

Abstract

Real-world biomedical scientific discovery operates as an iterative lifecycle integrating four core pillars: the targeted identification and analysis of question-specific omics datasets; the context-aware interpretation of molecular data using rich biomedical prior knowledge database; comprehensive literature reviews; and the subjective creativity and expert intuition of human scientists. Together, these pillars drive robust evidence synthesis and novel hypothesis generation. The recently reported AI agents support automated omics analysis and literature review, which typically require users to predefine and curate disease-specific datasets, which is a process that remains challenging and time-consuming. In this study, we present OmniCellAgent, a novel multi-agent AI framework built on large-scale single-cell RNA sequencing (scRNA-seq) datasets, and can autonomously retrieve and analyze disease and control-related scRNAseq datasets of diverse cell types across tissues and conditions. Moreover, it incorporates a biomedical prior knowledge and literature review agents, and disease domain-specific expert agents to systematically annotate omic data-derived targets. By aggregating evidence across agents, the framework generates structured analytical reports and potential scientific hypotheses. We evaluated OmniCellAgent across multiple disease settings, demonstrating its ability to identify relevant datasets, generate omic data analysis results, and produce structured reports and scientific hypotheses. Our findings demonstrate that multi-agent AI systems lower the technical barriers to omics-driven research, thereby accelerating scientific discovery and hypothesis generation in biomedical research and precision medicine. The source code is publicly available at: https://github.com/FuhaiLiAiLab/OmniCellAgent.