Graph Representation Learning on Tissue-Specific Multi-Omics

• URL: http://arxiv.org/abs/2107.11856v1
• Date: Sun, 25 Jul 2021 17:38:45 GMT
• Title: Graph Representation Learning on Tissue-Specific Multi-Omics
• Authors: Amine Amor (1), Pietro Lio' (1), Vikash Singh (1), Ramon Vi\~nas Torn\'e (1), Helena Andres Terre (1)
• Abstract summary: We leverage a graph embedding model (i.e VGAE) to perform link prediction on tissue-specific Gene-Gene Interaction (GGI) networks. We prove that the combination of multiple biological modalities (i.e multi-omics) leads to powerful embeddings and better link prediction performances.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Combining different modalities of data from human tissues has been critical in advancing biomedical research and personalised medical care. In this study, we leverage a graph embedding model (i.e VGAE) to perform link prediction on tissue-specific Gene-Gene Interaction (GGI) networks. Through ablation experiments, we prove that the combination of multiple biological modalities (i.e multi-omics) leads to powerful embeddings and better link prediction performances. Our evaluation shows that the integration of gene methylation profiles and RNA-sequencing data significantly improves the link prediction performance. Overall, the combination of RNA-sequencing and gene methylation data leads to a link prediction accuracy of 71% on GGI networks. By harnessing graph representation learning on multi-omics data, our work brings novel insights to the current literature on multi-omics integration in bioinformatics.

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