MODA: a graph convolutional network-based multi-omics integration framework for unraveling hub molecules and disease mechanisms.

ABSTRACT: Advances in omics technologies provide unprecedented opportunities for systems biology, yet integrating multi-omics data remains challenging due to its complexity, heterogeneity, and the sparsity of prior knowledge networks. Here, we introduce a multi-omics data integration analysis (MODA) framework that fully incorporates prior knowledge to identify hub molecules and pathways, and elucidate biological mechanisms. By leveraging multiple machine learning approaches, MODA transforms raw omics data into a feature importance matrix that is mapped onto a biological knowledge graph to mitigate omics data noise. Then, it uses graph convolutional networks with attention mechanisms to capture intricate molecular relationships and rank molecules via a feature-selective layer. Ultimately, MODA transc

SUBMITTER: Zhao J 

PROVIDER: S-EPMC12513173 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature