Drug Repositioning Based on Dual Multi-Graph Regularization Nonnegative Matrix Factorization and Multi-kernel Neural Network

Drug repositioning is a valuable and efficient strategy to discover new applications for traditional medications. In contrast to experimental methods, developing accurate and effective computational methods is crucial. The identification of potential drug-disease associations is a vital aspect of drug repositioning. In the paper, we proposed a new computational model called DDNMFNN to identify potential drug-disease associations, combining nonnegative matrix factorization and neural networks. The sparsity of validated drug-disease associations leads to subpar model generalization performance. To address this issue, a novel dual multi-graph regularization nonnegative matrix factorization algorithm with adaptive weights is proposed to reconstruct the association matrix. An efficient optimization algorithm is designed and convergence proof is provided. Furthermore, a multi-kernel neural network is utilized to predict potential associations based on the multiple similarity matrices and the reconstructed association matrix. This network effectively combines the nonparametric flexibility of the multi-kernel method with the structural characteristics of deep learning. The experimental results of 10-fold cross-validation demonstrate the proposed model achieved the best performance by comparing it with state-of-the-art models on three datasets. Case studies of three diseases and prediction results of five real-world network datasets further indicate that the proposed model as a precise prediction tool that can facilitate drug repositioning efforts effectively.