A genetic programming method for protein motif discovery and protein classification |
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Authors: | Denise Fukumi Tsunoda Alex Alves Freitas Heitor Silv??rio Lopes |
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Affiliation: | (1) Federal University of Parana, Av. Prefeito Loth?rio Meissner, 632, Room 38, Curitiba, PR, Brazil;(2) School of Computing, University of Kent, Room S107, Canterbury, Kent, CT2 7NF, UK;(3) Federal University of Technology, Av. 7 de Setembro, 3165, Bloco D, 3? floor, Curitiba, PR, Brazil |
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Abstract: | Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming
to establish common biological functions. This paper presents MAHATMA—memetic algorithm-based highly adapted tool for motif
ascertainment—a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur
very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the
classification of unknown proteins, that is, to predict their function by analyzing their primary structure. Experiments were
done with a set of enzymes extracted from the Protein Data Bank. The heuristic method used was based on genetic programming
using operators specially tailored for the target problem. The final performance was measured using sensitivity, specificity
and hit rate. The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is
effective in finding predictive features (motifs) for protein classification. |
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