Multi Omics Data

Multi-omics data integration aims to combine information from various biological data types (e.g., genomics, transcriptomics, proteomics) to gain a more comprehensive understanding of complex biological systems, particularly in disease research. Current research heavily utilizes machine learning, employing diverse architectures such as graph neural networks, transformers, and autoencoders, often incorporating techniques like contrastive learning and feature selection to address the high dimensionality and heterogeneity of these datasets. This integrated approach holds significant promise for improving disease diagnosis, prognosis, and treatment personalization, particularly in oncology, by revealing complex relationships between molecular profiles and clinical outcomes.