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.
@article{arxiv.2107.11856,
title = {Graph Representation Learning on Tissue-Specific Multi-Omics},
author = {Amine Amor and Pietro Lio' and Vikash Singh and Ramon Viñas Torné and Helena Andres Terre},
journal= {arXiv preprint arXiv:2107.11856},
year = {2021}
}
Comments
This paper was accepted at the 2021 ICML Workshop on Computational Biology