Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1371/JOURNAL.PCBI.1010404 | PLoS Computational Biology |
DocType | Volume | Issue |
Journal | 18 | 8 |
ISSN | Citations | PageRank |
1553-7358 | 0 | 0.34 |
References | Authors | |
28 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenxiang Zhang | 1 | 0 | 0.34 |
| Jialu Hou | 2 | 0 | 0.34 |
| Bin Liu | 3 | 419 | 33.30 |