A novel splice site prediction method using support vector machine

作者：Wei Dan; Zhang Huiling; Wei Yanjie^*; Jiang Qingshan

来源：Journal of Computational Information Systems, 2013, 9(20): 8053-8060.

DOI：10.12733/jcis6763

• Classification methods
• Distribution of tri-nucleotides
• F-score
• Feature selection methods
• Input sequence
• Markov model
• Splice site
• Splice Site Prediction

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.

• 出版日期2013
• 单位厦门大学