Machine Learning
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Exploring Alternative Splicing Features Using Support Vector Machines
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
Selection of vocal features for Parkinson's Disease diagnosis
International Journal of Data Mining and Bioinformatics
Support Vector Machines with L1 penalty for detecting gene-gene interactions
International Journal of Data Mining and Bioinformatics
International Journal of Data Mining and Bioinformatics
Multi-level clustering support vector machine trees for improved protein local structure prediction
International Journal of Data Mining and Bioinformatics
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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.