Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions

Understanding the role of genes in human disease is of high importance. However, identifying genes associated with human diseases requires laborious experiments that involve considerable effort and time. Therefore, a computational approach to predict candidate genes related to complex diseases including cancer has been extensively studied. In this study, we propose a convolutional neural network-based knowledge graph-embedding model (KGED), which is based on a biological knowledge graph with entity descriptions to infer relationships between biological entities. As an application demonstration, we generated gene-interaction networks for each cancer type using gene-gene relationships inferred by KGED. We then analyzed the constructed gene networks using network centrality measures, including betweenness, closeness, degree, and eigenvector centrality metrics, to rank the central genes of the network and identify highly correlated cancer genes. Furthermore, we evaluated our proposed approach for prostate, breast, and lung cancers by comparing the performance with that of existing approaches. The KGED model showed improved performance in predicting cancer-related genes using the inferred gene-gene interactions. Thus, we conclude that gene-gene interactions inferred by KGED can be helpful for future research, such as that aimed at future research on pathogenic mechanisms of human diseases, and contribute to the field of disease treatment discovery.

Automated summary

This study introduces a computational approach, KGED, based on convolutional neural networks to predict candidate genes related to complex diseases like cancer. By analyzing gene networks constructed with KGED, central genes were ranked using network centrality measures to identify highly correlated cancer genes. The model showed improved performance in predicting cancer-related genes, indicating potential for future research in understanding pathogenic mechanisms of human diseases and disease treatment discovery.