A neural collaborative filtering method for identifying miRNA-disease associations

摘要

The identification of disease-associated miRNAs can help people better understand the pathogenesis of diseases from a genetic perspective. Therefore, the prediction of miRNA-disease associations has received increasing attention. In this paper, we propose a new computational method NCFM (Neural network -based Collaborative Filtering Method) to predict miRNA-disease associations based on deep neural network. Firstly, high-dimensional sparse vectors of diseases and miRNAs are mapped into low -dimensional dense vectors in implicit semantic space via embedding layer, which called disease embedding and miRNA embedding, respectively. Secondly, the neural collaborative filter layers model the latent feature interactions between miRNAs and diseases. Then, different from other methods using square error loss function, we propose a new pairwise loss function to optimizes our model from a ranking perspective. Finally, experiments show that our proposed method can effectively prioritize disease-related miRNAs with the highest AUC of 0.912 and 0.921 compared with other recent methods in 5-fold cross validation and LOOCV framework. In addition, we implement two types of case studies, including four diseases. For a disease, more than 90% of predicted miRNAs are validated by another official dataset, which further illustrates the effectiveness of NCFM. (c) 2020 Published by Elsevier B.V.