
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.08.23.609465
preprint
1
Article
An explainable graph neural network approach for integrating multi-omics data with prior knowledge to identify biomarkers from interacting biological domains
Tripathy Rohit K. http://orcid.org/0000-0003-0808-1194

Frohock Zachary
Wang Hong
Cary Gregory A. http://orcid.org/0000-0003-3573-5229

Keegan Stephen http://orcid.org/0000-0002-0439-4727

Carter Gregory W. http://orcid.org/0000-0002-2834-8186

Li Yi
26 8 2024
2024.08.23.609465https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.08.23.609465
nihpp-2024.08.23.609465.pdf
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 is based on the use of 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 interest. We demonstrate the effectiveness of our approach by applying it to Alzheimer’s disease (AD) multi-omics data from the ROSMAP cohort, showing that the integration of transcriptomics and proteomics data with AD biological domain network priors improves the prediction accuracy of AD status and highlights robust AD biomarkers.
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