Computational Disease-Gene Association Prioritization Using Graph Neural Networks and Attention Mechanisms

Predicting disease-gene associations is crucial in human health for understanding the genetic basis of diseases, identifying potential therapeutic targets, and advancing personalized medicine. In this paper, we present a novel network-based method that integrates graph neural networks (GNNs), attention mechanisms, and random walks to accurately predict diseasegene associations. GNNs encode structural features to nodes in a heterogeneous graph, attention mechanisms compute the connectivity between the nodes, and random walks capture the changes between states within the graph. Experimental results demonstrated the superiority of the proposed model, achieving the area under the receiver operating characteristics curve of 0.982 and recall 0.907.