AI for Scientific Discovery in Omics Data-Driven Precision Medicine.

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Authors: Fuhai Li, Heming Zhang, Di Huang, Hao Li, Wenyu Li, Tianqi Xu, Yixin Chen, Michael A. Province, Philip Payne
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Abstract

In recent years, the rapid advancement of high-throughput technologies has led to the generation of vast and complex multi-omics datasets that are valuable for characterizing and understanding complex cell signaling network systems. On the other hand, large language models (LLMs), domain-specific foundation models (FMs) and AI agents, have achieved significant breakthroughs and have been revolutionizing scientific research. The convergence of these two trends is catalyzing a new era for biomedical research to augment and speed up scientific discovery and the development of precision medicine. In this study, we examine the large-scale omics datasets, emerging applications, and challenges at the intersection of massive omic datasets and related AI models and agents, highlighting how their integration is reshaping the landscape of biomedical research and precision medicine.

Fields of Study & Subjects

Bioinformatics and Genomic Networks Artificial Intelligence in Healthcare and Education Computational Drug Discovery Methods

Publication details

Journal / source
PubMed
Volume
123
Issue
1
Pages
55–66
Publication date
1 January 2026
Language
EN