IPiDA-LTR: Identifying Piwi-interacting RNA-disease Associations Based on Learning to Rank

Overview

Journal PLoS Comput Biol

Specialty Biology

Date 2022 Aug 15

PMID 35969645

Authors

Wenxiang Zhang

Jialu Hou

Bin Liu

Affiliations

Soon will be listed here.

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.

Citing Articles

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piRNA-disease association prediction based on multi-channel graph variational autoencoder.

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iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.

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