Prediction of lncRNA-Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network.

ABSTRACT: Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA-disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA-disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA-disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA-disease associations by integra

SUBMITTER: Li J 

PROVIDER: S-EPMC8763691 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature