School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China.
Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China.
Bioinformatics. 2019 Mar 15;35(6):1053-1054. doi: 10.1093/bioinformatics/bty738.
MicroRNAs (miRNAs) are one class of small noncoding RNA molecules, which regulate gene expression at the post-transcriptional level and play important roles in health and disease. To dissect the critical miRNAs in miRNAome, it is needed to predict the essentiality of miRNAs, however, bioinformatics methods for this purpose are limited.
Here we propose miES, a novel algorithm, for the prioritization of miRNA essentiality. miES implements a machine learning strategy based on learning from positive and unlabeled samples. miES uses sequence features of known essential miRNAs and performs miRNAome-wide searching for new essential miRNAs. miES achieves an AUC of 0.9 for 5-fold cross validation. Moreover, experiments further show that the miES score is significantly correlated with some established biological metrics for miRNA importance, such as miRNA conservation, miRNA disease spectrum width (DSW) and expression level.
The R source code is available at the download page of the web server, http://www.cuilab.cn/mies.
Supplementary data are available at Bioinformatics online.
MicroRNAs (miRNAs) 是一类小的非编码 RNA 分子，它们在转录后水平调节基因表达，在健康和疾病中发挥重要作用。为了剖析 miRNAome 中的关键 miRNA，需要预测 miRNA 的必需性，但目前用于此目的的生物信息学方法有限。
在这里，我们提出了 miES，一种用于 miRNA 必需性优先级排序的新算法。miES 实现了一种基于从阳性和未标记样本中学习的机器学习策略。miES 使用已知必需 miRNA 的序列特征，并在 miRNAome 中进行新的必需 miRNA 搜索。miES 在 5 倍交叉验证中达到了 0.9 的 AUC。此外，实验进一步表明，miES 得分与 miRNA 重要性的一些已建立的生物学指标显著相关，如 miRNA 保守性、miRNA 疾病谱宽度 (DSW) 和表达水平。
R 源代码可在网页服务器的下载页面获得，网址为 http://www.cuilab.cn/mies。
补充数据可在生物信息学在线获得。
