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Bioinformatics. 2011 May 15;27(10):1436-7. doi: 10.1093/bioinformatics/btr148. Epub 2011 Mar 23.

Prediction of novel pre-microRNAs with high accuracy through boosting and SVM.

Author information

1
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.

PMID:
21436129
DOI:
10.1093/bioinformatics/btr148
[Indexed for MEDLINE]

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