DEJKMDR: miRNA-disease association prediction method based on graph convolutional network - Gao, Shiyuan; Kuang, Zhufang; Duan, Tao; Deng, Lei (Frontiers in Medicine, 2023)
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 small size and high cost, so computational simulations are urgently required to forecast the potential correlation between miRNA and disease rapidly and accurately. In the paper, the DEJKMDR, a graph convolutional network (GCN)-based miRNA-disease association prediction model is proposed. The innovation 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 minimise overfitting, some edges are randomly destroyed during the training phase after DropEdge has been used to regularise the edges. JK-Net, meantime, 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 10-fold cross-validation, the average AUC of DEJKMDR is determined to be 0.9772.