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

ABSTRACT: 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, includin

SUBMITTER: Choi W 

PROVIDER: S-EPMC8519444 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature