Piwi相互作用RNA
稳健性(进化)
计算生物学
计算机科学
疾病
复杂疾病
生物信息学
机器学习
生物
医学
核糖核酸
RNA干扰
遗传学
病理
基因
作者
Kai Zheng,Xinlu Zhang,Lei Wang,Zhu‐Hong You,Bo-Ya Ji,Xianjun Liang,Zhengwei Li
摘要
piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. In this study, a novel computational model based on the structural perturbation method is proposed to predict potential disease-associated piRNAs, called SPRDA. Notably, SPRDA belongs to positive-unlabeled learning, which is unaffected by negative examples in contrast to previous approaches. In the 5-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.
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