Prediction of novel pre-microRNAs with high accuracy through boosting and SVM
Journal
Bioinformatics
Journal Volume
27
Journal Issue
10
Pages
1436-1437
Date Issued
2011
Author(s)
Zhang, Yuanwei
Yang, Yifan
Zhang, Huan
Jiang, Xiaohua
Xu, Bo
Xue, Yu
Cao, Yunxia
Zhai, Qian
Zhai, Yong
Xu, Mingqing
Cooke, Howard J.
Abstract
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.
SDGs
Type
journal article