Science
This academic review explores the integration of artificial intelligence (AI) with multiomics data in translational cancer research, highlighting its applications and future potential. The text discusses how AI methods analyze complex biological datasets from technologies like single-cell and spatial profiling to advance fundamental cancer biology, biomarker discovery, patient stratification, and drug development. It also addresses the current state of AI in clinical care, particularly in imaging and computational pathology, while acknowledging significant challenges. Key hurdles include ensuring reproducibility, interpretability of AI models, and seamless integration into clinical workflows, all crucial for delivering actionable insights and personalized cancer treatments. References: * Yates J, Van Allen E M. New horizons at the interface of artificial intelligence and translational cancer research[J]. Cancer Cell, 2025, 43(4): 708-727.