  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.
