Document Type
Article
Publication Date
5-8-2025
Original Citation
Tripathy R,
Frohock Z,
Wang H,
Cary G,
Keegan S,
Carter GW,
Li Y.
Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks. NPJ Syst Biol Appl. 2025;11(1):43.
Keywords
JGM, JMG, Humans, Neural Networks, Computer, Biomarkers, Alzheimer Disease, Proteomics, Computational Biology, Genomics, Gene Expression Profiling, Transcriptome, Algorithms, Graph Neural Networks, Multiomics
JAX Source
NPJ Syst Biol Appl. 2025;11(1):43.
ISSN
2056-7189
PMID
40341543
DOI
https://doi.org/10.1038/s41540-025-00519-9
Grant
YL and GWC were supported by NIA grant R21 AG083299. GWC was supported by NIA grant U54 AG065187.
Abstract
The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a limited number of samples. We propose a framework called GNNRAI for the supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the correlation structures among features from high-dimensional 'omics data, which reduces the effective dimensions in data and enables us to analyze thousands of genes simultaneously using hundreds of samples. Furthermore, our framework incorporates explainability methods to elucidate informative biomarkers. We apply our framework to Alzheimer's disease (AD) multi-omics data, showing that the integration of transcriptomics and proteomics data with prior AD knowledge is effective, improving the prediction accuracy of AD status over single-omics analyses and highlighting both known and novel AD-predictive biomarkers.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.