Prediction of novel pre-microRNAs with high accuracy through boosting and SVM |
| |
Authors: | Zhang Yuanwei Yang Yifan Zhang Huan Jiang Xiaohua Xu Bo Xue Yu Cao Yunxia Zhai Qian Zhai Yong Xu Mingqing Cooke Howard J Shi Qinghua |
| |
Affiliation: | 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. |
| |
Abstract: | 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. |
| |
Keywords: | |
本文献已被 PubMed 等数据库收录! |
|