INTEGRATIVE MACHINE LEARNING APPROACHES FOR MULTI-OMICS DATA ANALYSIS IN CANCER RESEARCH

Abstract

Integrative machine learning approaches have emerged as essential tools in the analysis of multi-omics data in cancer research, offering significant advancements in understanding complex biological systems. This review emphasizes recent progress in these techniques, highlighting their ability to manage the complexity and heterogeneity of multi-omics datasets, which include genomics, transcriptomics, proteomics, and metabolomics. By effectively integrating these diverse data types, machine learning approaches provide unprecedented insights into cancer mechanisms, facilitating the discovery of novel biomarkers and therapeutic targets. The review evaluates various machine learning methods, discussing their respective strengths and limitations in the context of cancer research. It also explores potential future directions for research, underscoring the need for continued methodological innovation and interdisciplinary collaboration to fully harness the power of integrative machine learning in advancing cancer treatment and personalized medicine.