School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China.
PLoS Comput Biol. 2022 Aug 15;18(8):e1010404. doi: 10.1371/journal.pcbi.1010404. eCollection 2022 Aug.
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.
Piwi 相互作用 RNA（piRNAs）被认为是疾病诊断和治疗的药物靶点和生物标志物。然而，生物实验需要耗费大量的时间和资源，并且现有的计算方法仅专注于识别已知 piRNAs 和疾病之间缺失的关联。随着生物实验的快速发展，越来越多的 piRNAs 被检测到。因此，识别新检测到的 piRNAs 的 piRNA-疾病关联具有重要的理论价值和实际意义，可以帮助我们了解疾病的发病机制。在这项研究中，我们提出了 iPiDA-LTR 预测器，该预测器基于学习排序来识别 piRNA 和疾病之间的关联。iPiDA-LTR 预测器不仅可以识别已知 piRNAs 和疾病之间缺失的关联，还可以检测与新检测到的 piRNAs 相关的疾病。实验结果表明，iPiDA-LTR 能够有效地预测 piRNA-疾病关联，优于其他相关方法。
