TY - JOUR
T1 - iPiDA-LTR
T2 - Identifying piwi-interacting RNA-disease associations based on Learning to Rank
AU - Zhang, Wenxiang
AU - Hou, Jialu
AU - Liu, Bin
N1 - Publisher Copyright:
Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85137125567&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1010404
DO - 10.1371/journal.pcbi.1010404
M3 - Article
C2 - 35969645
AN - SCOPUS:85137125567
SN - 1553-734X
VL - 18
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 8
M1 - e1010404
ER -