Prediction of miRNA and Disease Association based on Graph Convolution Network using Latent Feature Vector by Positive Samples

The traditional approach of wet biological experiments tends to reveal whether there is an association between specific miRNA molecules and diseases, resulting in a lack of reliable negative samples of miRNA-disease associations in existing databases. To deal with the problem that many current computational methods treat unknown miRNA-disease associations in benchmark datasets as negative samples directly, we propose a graph convolutional neural network model, named DNMFGCN-MDA, based on feature extractions of positive samples. Firstly, by only using a dynamic matrix with positive samples, we extracted the potential feature vectors U and V of miRNAs and diseases in low-dimensional space. Then, we combined known miRNA-disease associations with potential feature vectors U and V to construct heterogeneous graph neural networks. Finally, we adopted graph convolutional neural networks to learn the structural features of the heterogeneous graph network and predicted the potential miRNA-disease associations by using linkage prediction. We use a 5-fold cross-validation experiment to evaluate the performance of our mode, and it turns out that our model has an average AUC of 95.23% and 96.07% on the HMDD v2.0 and HMDD v3.2 datasets, respectively, and achieves better-associated performance than that of the comparison method.