Integrating multi-omics data: Methods and applications in human complex diseases

Over the past few decades, technological advancements and declining costs of high-throughput data generation have revolutionized biomedical research, enabling the collection of large-scale datasets across multiple omics layers—including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. The analysis and integration of these datasets provides global insights into biological processes and holds great promise in elucidating the myriad molecular interactions associated with human diseases, particularly multifactorial ones such as cancer, cardiovascular, and neurodegenerative disorders. However, integrating multi-omics data presents significant challenges due to high dimensionality and heterogeneity. This review explores computational methods for integrating multi-omics data, with a particular focus on network-based approaches that offer a holistic view of relationships among biological components in health and disease. Furthermore, this review showcases a selection of recent, successful applications of multi-omics data integration, moving beyond theoretical methods to demonstrate their transformative potential in biomarker discovery, patient stratification, and guiding therapeutic interventions in specific human diseases.