MGAMDA: Multi Source Similarity Fusion-Based Graph Convolutional Neural Network and Attention Mechanism Network for Predicting MiRNA-Disease Associations

Résumé fourni par la source

A mounting body of research indicates that dysregulation of MicroRNAs (miRNAs) causes disease through a variety of underlying mechanisms. Predicting microRNA (miRNA)-disease associations (MDAs) is essential for disease prognosis and therapeutics. Compared to conventional biological experiments, computational models save time and effort. A new method is proposed inspired by the graph convolutional networks. It has been named Multi source similarity fusion-based graph convolutional neural network and attention mechanism network for predicting miRNA-disease associations (MGAMDA). First, the several similarity networks between miRNAs and diseases were built. Then, multi-source information network is fused. And the feature was aggregated by using GCNs. In order to address the different levels of importance of the information, an attention mechanism was used to assign weights. The similar features of the disease side and miRNA side were finally obtained separately. It is combined with the association features that are obtained from the association information, and then it is fed into the multi-layer perceptron (MLP). To obtain prediction scores for unknown associations between miRNAs and diseases, a multilayer perceptron was utilized. To validate the new methodology's effectiveness, we performed a series of experimental studies using the Human MicroRNA Disease Database (HMDD v3.2). The performance of the$\mathbf{5}$-fold cross-validation on the datasets shows that MGAMDA surpasses other methods in the area of AUC, AUPR, ACC, F1-score, Recall, and Precision. Furthermore, case studies have demonstrated that MGAMDA accurately predicts miRNAs associated with colon, breast, and stomach cancer.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.