Multiomics data integration tools for biomarkers discovery

Abstract

Biomarkers are essential tools in modern medicine and research, enabling early disease detection, precise classification, and tailored therapeutic interventions. The integration of multi-omics data has transformed biomarker discovery, allowing researchers to uncover intricate molecular interactions and pathways. This report explores the methodologies and applications of leading tools such as DIABLO, MINT, JIVE, and MOFA+, emphasizing their role in analyzing complex biological datasets and identifying clinically relevant biomarkers. A comprehensive case study highlights the use of DIABLO for breast cancer subtyping, where extensive hyperparameter tuning was conducted to optimize model performance. This process minimized error rates and improved the identification of subtype-specific biomarkers, demonstrating the tool’s robustness and translational potential. The report further discusses challenges, including scalability and reproducibility, and proposes future directions, such as hybrid frameworks and AI-driven approaches, to advance biomarker discovery. These findings underscore the transformative potential of biomarker research in healthcare, precision medicine, and beyond.