通过提升算法和 SVM 精准预测新型前 microRNAs。
Prediction of novel pre-microRNAs with high accuracy through boosting and SVM.
机构信息
Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.
出版信息
Bioinformatics. 2011 May 15;27(10):1436-7. doi: 10.1093/bioinformatics/btr148. Epub 2011 Mar 23.
UNLABELLED
High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species.
AVAILABILITY
miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php.
未加标签
高通量深度测序技术产生了数量空前的表达短序列读段，这不仅带来了机会，也对新 microRNA 的预测提出了挑战。为了验证候选 microRNA 的存在，我们必须证明这些短序列可以从候选前 microRNA 中加工而来。然而，使用现有的实验技术来验证这些是费力且耗时的。因此，在这里，我们描述了一种新的方法 miRD，它使用基于支持向量机（SVM）和提升方法的两种特征选择策略构建。它是一种高效的新的前 microRNA 预测工具，在不同物种中的准确率高达 94.0%。
可利用性
miRD 是用 PHP/PERL+MySQL+R 实现的，可以在 http://mcg.ustc.edu.cn/rpg/mird/mird.php 上免费访问。
Zotero 插件