MACHINE LEARNING APPROACHES FOR INTEGRATIVE MULTI-OMICS DATA ANALYSIS OF CANCER: A SYSTEMATIC REVIEW

In biomedical research, High-throughput omics measurement platforms are crucial in generating massive amounts of data from various biological sources. Leveraging machine learning-based predictive algorithms, researchers can integrate and analyze this extensive data to gain valuable insights into complex biological systems. Machine learning skills are utilized in multi-omics studies to develop predictive models for disease diagnosis, prognosis, and treatment response. By integrating data from genomics, proteomics, metabolomics, and other omics disciplines, these advanced algorithms can identify potential biomarkers that may have been overlooked using traditional analytical methods. This review delves into the integrative machine learning methods employed to comprehend the intricate interactions within biological systems. By harnessing these techniques, interdisciplinary professionals can gain a deeper understanding of the underlying mechanisms governing various physiological and pathological processes. An essential focus of this review is on exploring the potential of machine learning methods to integrate multi-omics data effectively and extract meaningful biological signals. These signals could hold the key to uncovering novel therapeutic targets or diagnostic markers for a wide range of diseases. The review also provides insightful recommendations and addresses the challenges associated with applying machine learning approaches to the study of biological systems. It offers guidance for overcoming obstacles related to data integration, model interpretation, and validation, ultimately fostering advancements in biomedical research and clinical practice.