Comparing several approaches for hierarchical classification of proteins with decision trees

Authors:
Eduardo P. Costa;Ana C. Lorena;André C. P. L. F. Carvalho;Alex A. Freitas;Nicholas Holden
Affiliations:
Depto. Ciências de Computação, ICMC/USP, Sao Carlos, SP, Brazil;Universidade Federal do ABC, Santo André, SP, Brazil;Depto. Ciências de Computação, ICMC/USP, Sao Carlos, SP, Brazil;Computing Laboratory, University of Kent, Canterbury, UK;Computing Laboratory, University of Kent, Canterbury, UK
Venue:
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
Year:
2007

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Abstract

Proteins are the main building blocks of the cell, and perform almost all the functions related to cell activity. Despite the recent advances in Molecular Biology, the function of a large amount of proteins is still unknown. The use of algorithms able to induce classification models is a promising approach for the functional prediction of proteins, whose classes are usually organized hierarchically. Among the machine learning techniques that have been used in hierarchical classification problems, one may highlight the Decision Trees. This paper describes the main characteristics of hierarchical classification models for Bioinformatics problems and applies three hierarchical methods based on the use of Decision Trees to protein functional classification datasets.