Integrating Multi-Omics Data Using AI

The convergence of AI and multi-omics technologies is revolutionizing pharmacology and precision medicine, overcoming traditional limitations by integrating genomics, transcriptomics, proteomics, and metabolomics for holistic drug interaction analysis. AI methods like random forests, support vector machines, deep learning, and multimodal transformers enable biomarker discovery, drug response prediction, patient stratification, and digital twins. Oncology advances tumor profiling, cardiovascular medicine predicts heart failure efficacy, and rare diseases identify targets despite data scarcity. Challenges include data privacy, algorithmic bias, regulatory hurdles, and inequities excluding under-resourced populations. Future integration with XAI, EHRs, and wearables promises interpretable, patient-centered care, shifting from reductionist to systems paradigms.