School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac224.
Identifying miRNA-disease associations is an important task for revealing pathogenic mechanism of complicated diseases. Different computational methods have been proposed. Although these methods obtained encouraging performance for detecting missing associations between known miRNAs and diseases, how to accurately predict associated diseases for new miRNAs is still a difficult task. In this regard, a ranking framework named idenMD-NRF is proposed for miRNA-disease association identification. idenMD-NRF treats the miRNA-disease association identification as an information retrieval task. Given a novel query miRNA, idenMD-NRF employs Learning to Rank algorithm to rank associated diseases based on high-level association features and various predictors. The experimental results on two independent test datasets indicate that idenMD-NRF is superior to other compared predictors. A user-friendly web server of idenMD-NRF predictor is freely available at http://bliulab.net/idenMD-NRF/.
识别 miRNA-疾病关联对于揭示复杂疾病的发病机制非常重要。已经提出了不同的计算方法。尽管这些方法在检测已知 miRNAs 与疾病之间缺失的关联方面取得了令人鼓舞的性能，但如何准确预测新 miRNAs 的相关疾病仍然是一项艰巨的任务。在这方面，提出了一种名为 idenMD-NRF 的排名框架，用于 miRNA-疾病关联识别。idenMD-NRF 将 miRNA-疾病关联识别视为信息检索任务。给定一个新的查询 miRNA，idenMD-NRF 使用学习排序算法基于高级关联特征和各种预测器对相关疾病进行排序。在两个独立的测试数据集上的实验结果表明，idenMD-NRF 优于其他比较预测器。idenMD-NRF 预测器的用户友好型网络服务器可免费在 http://bliulab.net/idenMD-NRF/ 上获得。
