Title: Prediction of alternatively spliced exons using Support Vector Machines

 

Author: Jing Xia, Doina Caragea, Susan J. Brown

 

Addresses:
Department of Computing and Information Sciences, Kansas State University, 234 Nichols Hall, Manhattan, KS 66506, USA.
Department of Computing and Information Sciences, Kansas State University, 234 Nichols Hall, Manhattan, KS 66506, USA.
Divison of Biology, Kansas State University, 239A Chalmers Hall, Manhattan, KS 66506, USA

 

Journal: Int. J. of Data Mining and Bioinformatics, 2010 Vol.4, No.4, pp.411 - 430

 

Abstract: Alternative splicing is a mechanism for generating different gene transcripts (called isoforms) from the same genomic sequence. In this paper, we explore the predictive power of a large set of diverse gene features that have been experimentally shown to have effect on alternative splicing. We use such features to build support vector machine classifiers for predicting alternatively spliced exons. Experimental results show that classifiers built from the diverse set of features give better results than those that consider only basic sequence features. Furthermore, we use feature selection methods to identify the most informative features for the prediction problem at hand.

 

Keywords: pre-mRNA alternative splicing; feature construction; splicing motifs; SVM classifiers; support vector machines; bioinformatics; alternatively spliced exons; gene transcripts; isoforms; genomic sequences; classification.

 

DOI: http://dx.doi.org/10.1504/IJDMB.2010.034197

 

Available online 17 Jul 2010

 

 

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