Title: Graph Representation Learning on Tissue-Specific Multi-Omics Show Chinese title

Title: 组织特异性多组学图表示学习

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. Show Chinese abstract

Abstract: 结合来自人体组织的不同数据模态在推进生物医学研究和个性化医疗护理方面至关重要。 在本研究中，我们利用图嵌入模型（即VGAE）对组织特异性基因-基因相互作用（GGI）网络进行链接预测。 通过消融实验，我们证明多种生物模态的组合（即多组学）能够产生强大的嵌入并提高链接预测性能。 我们的评估表明，基因甲基化谱和RNA测序数据的整合显著提高了链接预测性能。 总体而言，RNA测序和基因甲基化数据的结合在GGI网络上的链接预测准确率达到71%。 通过在多组学数据上利用图表示学习，我们的工作为生物信息学中多组学整合的现有文献带来了新的见解。