Advancing AI for multi-omics and clinical data integration in basic and translational cancer research

A comprehensive review published in Nature Reviews Cancer examines how artificial intelligence can be leveraged to integrate multi-omics data — including genomics, transcriptomics, proteomics, and metabolomics — to unlock deeper insights into cancer biology and treatment response. Liu and colleagues argue that analyses limited to a single omics layer risk missing critical dimensions of biological regulation that drive tumor complexity and patient variability. The review highlights AI-driven frameworks that combine diverse molecular and clinical datasets to enable patient-specific modeling of cancer mechanisms, potentially advancing precision oncology. Published in a top-tier oncology journal, this work provides a timely roadmap for researchers and clinicians seeking to harness multi-modal data integration for improved translational outcomes.