A multi-layer multi-kernel neural network for determining associations between non-coding RNAs and diseases

Abstract

Identification of associations between non-coding RNAs and diseases plays an important role in the study of pathogenesis, which has been a hot topic in recent research. However, traditional methods are time-consuming to detect the associations between non-coding RNAs and diseases. Recently, associations of non-coding RNAs and diseases can be regarded as bipartite network. In this paper, we propose a novel deep multiple kernel learning method, called the multi-layer multi-kernel deep neural network (MLMKDNN). First, many feature matrices are built by multiple features of non-coding RNAs and diseases. Then, these feature matrices are mapped into kernel space and fused by deep neural network. Finally, combine two fused output of MLMKDNN as the predicted values. Three types of non-coding RNAs (miRNA, circRNA and lncRNA) are used to test the performance of MLMKDNN. Compared with other existing methods, our proposed model has high Area Under Precision Recall (AUPR) value on three types of datasets. Experimental results confirm that our method is an effective predictive tool. It provides a framework that can also be applied to the link prediction of other bipartite networks.