Graph Representation Learning on Tissue-Specific Multi-Omics
This work addresses the challenge of multi-omics integration in bioinformatics for biomedical research and personalized medical care, but it is incremental as it applies an existing graph embedding model to new data combinations.
The study tackled the problem of improving link prediction in tissue-specific Gene-Gene Interaction networks by integrating multiple biological modalities, resulting in a link prediction accuracy of 71% when combining RNA-sequencing and gene methylation data.
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.