# Graph Representation Learning on Tissue-Specific Multi-Omics

arXiv: https://arxiv.org/abs/2107.11856 · HTML: https://mlsanity.com/paper/2107.11856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 25 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Predicting multicellular function through multi-layer tissue networks](https://mlsanity.com/paper/1707.04638/) (2017)
- [Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction](https://mlsanity.com/paper/1907.05628/) (2019)

## Built on by (most cited first, 1 of 1)

- [Multimodal Learning for Multi-Omics: A Survey](https://mlsanity.com/paper/2211.16509/) (2022)

## Abstract (arXiv)

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.