iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank

Abstract

Piwi-interacting RNAs (piRNAs) are regarded as drug targets and biomarkers for the diagnosis and therapy of diseases. However, biological experiments cost substantial time and resources, and the existing computational methods only focus on identifying missing associations between known piRNAs and diseases. With the fast development of biological experiments, more and more piRNAs are detected. Therefore, the identification of piRNA-disease associations of newly detected piRNAs has significant theoretical value and practical significance on pathogenesis of diseases. In this study, the iPiDA-LTR predictor is proposed to identify associations between piRNAs and diseases based on Learning to Rank. The iPiDA-LTR predictor not only identifies the missing associations between known piRNAs and diseases, but also detects diseases associated with newly detected piRNAs. Experimental results demonstrate that iPiDA-LTR effectively predicts piRNA-disease associations outperforming the other related methods.

Author summary

Accumulating evidences have indicated that dysfunction and abnormal expression of piRNAs are closely associated with the emergence and development of diseases. Currently, identifying piRNA-disease associations mainly focuses on biological experimental methods and computational methods. However, biological experimental methods take substantial time and resources. Computational methods mainly focused on identifying diseases associated known piRNAs. With the development of biological technology, more and more newly detected piRNAs were detected. Therefore, identifying diseases associated with newly detected piRNAs is more important compared with identifying diseases associated with known piRNAs. Information retrieval (IR)’s goal is to rank documents based on the relevance to certain topics. This task is particularly similar with identification of piRNA-disease associations. Specifically, ranking documents related to previous topics corresponds to identify diseases associated with known piRNAs, and ranking documents related to novel topics is similar to identify diseases associated with newly detected piRNAs. Therefore, we propose a new predictor called iPiDA-LTR to predict associations between piRNAs and diseases based on information retrieval technology. Experimental results indicated that iPiDA-LTR is promising in identifying diseases associated with known piRNAs and newly detected piRNAs.