Link Prediction in Disease-Disease Interactions Network Using a Hybrid Deep Learning Model

EasyChair Preprint 15898

Abstract

Discovering disease-disease association based on the underlying biological mechanisms is an essential biomedical task in modern biology as understanding these relationships will assist biologists in discovering the pathogenesis, diagnosis, and intervention of human diseases. Recently, deep learning on graph and graph neural networks have achieved promising performance in modeling complex biological structures and learning compact representations of interconnected data. Inspired by the success of graph neural networks in learning subgraph representations, we propose a novel framework, SNN-VGA, designed to predict potential disease comorbid pairs. We first model disease-associated genes as subgraphs in the protein-protein interactions network and learn disentangled disease module representations using a subgraph neural network model. The learned embeddings are leveraged by the variational graph auto-encoder to predict disease comorbidity in the disease-disease interactions network. Empirical results from a benchmark dataset demonstrate that our method performs competitively compared with the state-of-the-art model, with an AUROC of 0.96.

Keyphrases: Anchor Patch, Association, Comorbidity, Disease, Disease graph, Disease modules, Disease-associated genes, GCN, Graph Neural Networks, PPIs Network, Subgraph neural network, VGAE, Variational graph auto-encoder, all disease modules, connected component, connected components, disease associations, disease comorbidity prediction, disease disease interactions network, disease feature vectors, disease module, disease module representations, disease network, disease related genes, disease similarity based on gene, disease subgraph, disease-gene associations, graph structure, hybrid deep learning model, hybrid deep learning model for disease disease, learning model for disease disease link prediction, link prediction, networks, neural, predict disease comorbidity, prediction, protein protein interactions ppis, subgraph, subgraph component, subgraph neural networks variational graph auto, used for comorbidity prediction

@booklet{EasyChair:15898,
  author    = {Ashwag Altayyar and Li Liao},
  title     = {Link Prediction in Disease-Disease Interactions Network Using a Hybrid Deep Learning Model},
  howpublished = {EasyChair Preprint 15898},
  year      = {EasyChair, 2025}}