Human splice site identification with multiclass support vector machines and bagging

Authors:
Ana Carolina Lorena;André C. P. L. F. de Carvalho
Affiliations:
Laboratório de Inteligência Computacional, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, São Paulo, Brasil;Laboratório de Inteligência Computacional, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, São Paulo, Brasil
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

The complete identification of human genes involves determining parts that generates proteins, named exons, and those that do not code for proteins, known as introns. The splice site identification problem is concerned with the recognition of the boundaries between these regions. This work investigates the use of Support Vector Machines (SVMs) in human splice site identification. Two methods employed for building multiclass SVMs, one-against-all and all-against-all, were compared. For this application, the all-against-all method obtained lower classification error rates. Ensembles of multiclass SVMs with Bagging were also evaluated. Against the expected, the use of ensembles did not improve the performance obtained.