A novel splice site prediction method using support vector machine

被引：0
作者：
机构：
[1] Cognitive Science Department and Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University
[2] Shenzhen Key Lab. for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
来源：
Wei, Y. (yj.wei@siat.ac.cn) | 1600年 / Binary Information Press, P.O. Box 162, Bethel, CT 06801-0162, United States卷 / 09期
关键词：
Distribution of tri-nucleotides; Markov model; Splice site; Support vector machine;
D O I：
10.12733/jcis6763
中图分类号：
学科分类号：
摘要：
We present a novel classification method for splice sites prediction using support vector machine (SVM). The method first represents input sequences by sequence-based features, including the information of the distribution of tri-nucleotides and the conserved features surrounding the splice sites characterized by Markov model. An F-score based feature selection method is then used to select informative features to improve the performance. Finally, SVM is employed to classify the splice sites with the selected features. Experimental results show that this method improves splice site prediction accuracy and performs better than the existing methods such as MM1-SVM, Reduced MM1-SVM and some other methods. © 2013 Binary Information Press.
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页码：8053 / 8060
页数：7
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