DFL-PiDA: Prediction of Piwi-interacting RNA-Disease Associations based on Deep Feature Learning

Piwi-interacting RNAs (piRNAs) fulfill the necessary requirements of epigenetic mechanisms, working to regulate gene expression in diseases and homeostasis in a coordinated manner. Hence, predicting new piRNAs that are associated with diseases conduces to understanding the pathogenicity mechanisms. In this study, we presented a deep feature learning model (DFLPiDA) to predict potential piRNA-disease associations based on the multi-model similarity features of piRNAs and diseases and the convolutional denoising auto-encoder. In particular, we firstly calculated four types of similarity features of piRNAs and diseases. Then, the convolutional denoising auto-encoder was utilized to perform deep learning on the fused similarity features. Finally, the extreme learning machine was employed as the training model as well as to predict unknown associations. The empirical results of five-fold cross-validation experiments show that the DFL-PiDA is efficient for predicting potential piRNA-disease associations. Furthermore, we proved the effectiveness of convolutional denoising auto-encoder neural network in piRNA and disease association prediction. Case studies also demonstrate the practical application of DFL-PiDA to discover potential associations.