MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction.

ABSTRACT: Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrelationships among different categories of omics data. Numerous models have been suggested using graph-based learning to uncover veiled representations and network formations unique to distinct types of omics data to heighten predictions regarding cancers and characterize patients' profiles, amongst other applications aimed at improving disease management in medical research. The existing graph-based state-of-the-art multi-omics integration approaches for cancer subtype prediction, MOGONET, and SU

SUBMITTER: Tanvir RB 

PROVIDER: S-EPMC10932030 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature