Prediction of novel pre-microRNAs with high accuracy through boosting and SVM
作者： Yuanwei Zhang , Yifan Yang , Huan Zhang , Xiaohua Jiang , Bo Xu
DOI: 10.1093/BIOINFORMATICS/BTR148
关键词: Boosting (machine learning) 、 Support vector machine 、 Data mining 、 Expressed sequence tag 、 Machine learning 、 Feature selection 、 Computer science 、 Artificial intelligence
摘要: Summary: High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only opportunity but also a challenge for prediction novel microRNAs. To verify the existence candidate microRNAs, we have to show that these sequences can be processed from pre-microRNAs. However, it is laborious and time consuming using existing experimental techniques. Therefore, here, describe new method, miRD, which constructed two feature selection strategies based on support vector machines (SVMs) boosting method. It high-efficiency tool pre-microRNA with accuracy up 94.0% among different species. Availability: miRD implemented in PHP/PERL+MySQL+R freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php. Contact: qshi@ustc.edu.cn Supplementary information:Supplementary data are available Bioinformatics online.
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