Support vector machine approach for retained introns prediction using sequence features

Authors:
Huiyu Xia;Jianning Bi;Yanda Li
Affiliations:
MOE Key Laboratory of Bioinformatics / Department of Automation, Tsinghua University, Beijing, China;MOE Key Laboratory of Bioinformatics / Department of Automation, Tsinghua University, Beijing, China;MOE Key Laboratory of Bioinformatics / Department of Automation, Tsinghua University, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
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Making large-scale support vector machine learning practical

Advances in kernel methods
Accurate identification of alternatively spliced exons using support vector machine

Bioinformatics

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Abstract

It is estimated that 40-60% of human genes undergo alternative splicing. Currently, expressed sequence tags (ESTs) alignment and microarray analysis are the most efficient methods for large-scale detection of alternative splice events. Because of the inherent limitation of these methods, it is hard to detect retained introns using them. Thus, it is highly desirable to predict retained introns using only their own sequence information. In this paper, support vector machine is introduced to predict retained introns merely based on their own sequences. It can achieve a total accuracy of 98.54%. No other data, such as ESTs, are required for the prediction. The results indicate that support vector machine can achieve a reasonable acceptant prediction performance for retained introns with effective rejection of constitutive introns.