DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

Shiyuan Gao; Zhufang Kuang; Tao Duan; Lei Deng

doi:10.3389/fmed.2023.1234050

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

Front Med (Lausanne). 2023 Sep 12:10:1234050. doi: 10.3389/fmed.2023.1234050. eCollection 2023.

Authors

Shiyuan Gao¹, Zhufang Kuang¹, Tao Duan¹, Lei Deng²

Affiliations

¹ School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China.
² School of Computer Science and Engineering, Central South University, Changsha, China.

Abstract

Numerous studies have shown that miRNAs play a crucial role in the investigation of complex human diseases. Identifying the connection between miRNAs and diseases is crucial for advancing the treatment of complex diseases. However, traditional methods are frequently constrained by the small sample size and high cost, so computational simulations are urgently required to rapidly and accurately forecast the potential correlation between miRNA and disease. In this paper, the DEJKMDR, a graph convolutional network (GCN)-based miRNA-disease association prediction model is proposed. The novelty of this model lies in the fact that DEJKMDR integrates biomolecular information on miRNA and illness, including functional miRNA similarity, disease semantic similarity, and miRNA and disease similarity, according to their Gaussian interaction attribute. In order to minimize overfitting, some edges are randomly destroyed during the training phase after DropEdge has been used to regularize the edges. JK-Net, meanwhile, is employed to combine various domain scopes through the adaptive learning of nodes in various placements. The experimental results demonstrate that this strategy has superior accuracy and dependability than previous algorithms in terms of predicting an unknown miRNA-disease relationship. In a 10-fold cross-validation, the average AUC of DEJKMDR is determined to be 0.9772.

Keywords: DropEdge; JK-net; graph convolutional network; miRNA; miRNA-disease.