Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing rapidly, it is important to computationally identify new piRNA-disease associations with low cost and provide candidate piRNA targets for disease treatment. However, it is a challenging problem to learn effective association patterns from the positive piRNA-disease associations and the large amount of unknown piRNA-disease pairs. In this study, we proposed a computational predictor called iPiDi-PUL to identify the piRNA-disease associations. iPiDi-PUL extracted the features of piRNA-disease associations from three biological data sources, including piRNA sequence information, disease semantic terms and the available piRNA-disease association network. Principal component analysis (PCA) was then performed on these features to extract the key features. The training datasets were constructed based on known positive associations and the negative associations selected from the unknown pairs. Various random forest classifiers trained with these different training sets were merged to give the predictive results via an ensemble learning approach. Finally, the web server of iPiDi-PUL was established at http://bliulab.net/iPiDi-PUL to help the researchers to explore the associated diseases for newly discovered piRNAs.

中文翻译：

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iPiDi-PUL：基于未标记的正学习识别与 Piwi 相互作用的 RNA 疾病关联。

积累的研究表明，Piwi-interacting RNAs (piRNAs) 正在调控生殖细胞和干细胞的发育，并且与许多疾病的进展密切相关。随着检测到的 piRNA 数量迅速增加，重要的是以低成本计算识别新的 piRNA 与疾病的关联，并为疾病治疗提供候选的 piRNA 靶标。然而，从阳性 piRNA-疾病关联和大量未知的 piRNA-疾病对中学习有效的关联模式是一个具有挑战性的问题。在这项研究中，我们提出了一种称为 iPiDi-PUL 的计算预测器来识别 piRNA 与疾病的关联。iPiDi-PUL 从三个生物数据源中提取了 piRNA 与疾病关联的特征，包括 piRNA 序列信息、疾病语义术语和可用的 piRNA-疾病关联网络。然后对这些特征进行主成分分析 (PCA) 以提取关键特征。训练数据集是基于已知的正关联和从未知对中选择的负关联构建的。使用这些不同训练集训练的各种随机森林分类器被合并以通过集成学习方法给出预测结果。最后，iPiDi-PUL 的网络服务器在 http://bliulab.net/iPiDi-PUL 建立，以帮助研究人员探索新发现的 piRNA 的相关疾病。训练数据集是基于已知的正关联和从未知对中选择的负关联构建的。使用这些不同训练集训练的各种随机森林分类器被合并以通过集成学习方法给出预测结果。最后，iPiDi-PUL 的网络服务器在 http://bliulab.net/iPiDi-PUL 建立，以帮助研究人员探索新发现的 piRNA 的相关疾病。训练数据集是基于已知的正关联和从未知对中选择的负关联构建的。使用这些不同训练集训练的各种随机森林分类器被合并以通过集成学习方法给出预测结果。最后，iPiDi-PUL 的网络服务器在 http://bliulab.net/iPiDi-PUL 建立，以帮助研究人员探索新发现的 piRNA 的相关疾病。