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. Keywords. protein cellular localization, Machine Learning, multiclass Support Vector Machines, Decision Trees.
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