IPiDA-sHN: Identification of Piwi-interacting RNA-disease associations by selecting high quality negative samples. (October 2020)

• Record Type: Journal Article
• Authors: Wei, Hang; Ding, Yuxin; Liu, Bin
• Is Part Of: Computational biology and chemistry. Volume 88(2020)
• Publication Date: 2020-10
• Subjects: piRNA-disease associations -- Convolutional neural network -- High quality negative sample -- Positive-unlabeled learning; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107361
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Piwi-interacting RNAs (piRNAs) are associated with various diseases. There are limited positive piRNAs -disease associations and many unlabeled pairs. Hidden pair features were extracted by Convolutional Neural Network. Two-step positive-unlabeled learning was used to select high-quality negative pairs. Predictor trained with high-quality negative pairs achieved better performance. Abstract: As a large group of small non-coding RNAs (ncRNAs), Piwi-interacting RNAs (piRNAs) have been detected to be associated with various diseases. Identifying disease associated piRNAs can provide promising candidate molecular targets to promote the drug design. Although, a few computational ensemble methods have been developed for identifying piRNA-disease associations, the low-quality negative associations even with positive associations used during the training process prevent the predictive performance improvement. In this study, we proposed a new computational predictor named iPiDA-sHN to predict potential piRNA-disease associations. iPiDA-sHN presented the piRNA-disease pairs by incorporating piRNA sequence information, the known piRNA-disease association network, and the disease semantic graph. High-level features of piRNA-disease associations were extracted by the Convolutional Neural Network (CNN). Two-step positive-unlabeled learning strategy based on Support Vector Machine (SVM) was employed to select the high quality negative samples from the unknown piRNA-disease pairs. Finally, the SVM predictor trained with the known piRNA-disease associations and the high quality negative associations was used to predict new piRNA-disease associations. The experimental results showed that iPiDA-sHN achieved superior predictive ability compared with other state-of-the-art predictors.