AI-Driven Multi-Omics Integration in Oncology: A Paradigm Shift in Biomarker Discovery and Personalized Therapeutics

The convergence of artificial intelligence (AI) and multi-omics technologies is catalyzing a fundamental transformation in precision oncology. This integration addresses the core challenge of tumor heterogeneity by enabling the synthesis of complex, high dimensional data from genomics, transcriptomics, proteomics, metabolomics, and epigenomics. This review systematically evaluates the period from ۲۰۲۳ to ۲۰۲۵, highlighting how advanced machine learning (ML), deep learning (DL), and graph neural networks (GNNs) are not only processing but intelligently fusing these data layers to uncover latent biomarkers and biological mechanisms. We document significant applications in early cancer detection via enhanced liquid biopsy analytics, precision prediction of immunotherapy response, and the discovery of novel therapeutic vulnerabilities. Despite the transformative potential, critical challenges in data standardization, model interpretability, and clinical validation persist. We conclude by outlining a forward looking framework incorporating explainable AI (XAI), federated learning, and quantum enhanced computation, which collectively promise to accelerate the translation of AI multi-omics insights into robust, equitable, and actionable clinical tools, ultimately moving oncology towards a more proactive and personalized future.

کلیدواژه ها:

Artificial Intelligence ، Multi-Omics ، Precision Oncology ، Biomarkers ، Deep Learning ، Translational Bioinformatics