Protein cellular localization with multiclass support vector machines and decision trees

Authors:
Ana Carolina Lorena;André C. P. L. F. de Carvalho
Affiliations:
Instituto de Ciências Matemáticas e de Computação (ICMC), Universidade de São Paulo (USP), São Carlos, São Paulo, Brasil;Instituto de Ciências Matemáticas e de Computação (ICMC), Universidade de São Paulo (USP), São Carlos, São Paulo, Brasil
Venue:
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
Year:
2005

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Abstract

Many cellular functions are carried out in compartments of the cell. The cellular localization of a protein is thus related to its function identification. This paper investigates the use of two Machine Learning techniques, Support Vector Machines (SVMs) and Decision Trees (DTs), in the protein cellular localization prediction problem. Since the given task has multiple classes and SVMs are originally designed for the solution of two class problems, several strategies for multiclass SVMs extension were investigated, including one proposed by the authors.