An explainable graph neural network approach for effectively integrating multi-omics with prior knowledge to identify biomarkers from interacting biological domains.

ABSTRACT: The rapid growth of multi-omics datasets, in addition to the wealth of existing biological prior knowledge, necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying disease-related molecular markers. We propose a framework for supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the relationships among features from high-dimensional 'omics data and set transformers to integrate low-dimensional representations of 'omics features. Furthermore, our framework incorporates explainability methods to elucidate important biomarkers and extract interaction relationships between biological quantities of i

SUBMITTER: Tripathy RK 

PROVIDER: S-EPMC11383059 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature