Omics Specific Module

Omics data integration aims to leverage the combined information from multiple high-throughput datasets (e.g., genomics, transcriptomics, proteomics) for improved biological understanding and clinical applications, particularly in cancer research. Current research focuses on developing robust computational methods, such as graph neural networks and self-supervised learning models (including contrastive learning and multi-modal architectures), to handle incomplete and high-dimensional omics data effectively, even with limited labeled samples. These advancements enable more accurate patient stratification, improved cancer subtype classification, and enhanced interpretation of complex multi-omics relationships, ultimately contributing to more precise and personalized medicine.