Prediction of alternatively spliced exons using support vector machines

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.