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Multi-class Protein Sequence Classification Using Fuzzy ARTMAP

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3 Author(s)
Shakir Mohamed ; School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa. ; David Rubin ; Tshilidzi Marwala

The classification of protein sequences into families is an important tool in the annotation of structural and functional properties to newly discovered proteins. We present a classification system using pattern recognition techniques to create a numerical vector representation of a protein sequence and then classify the sequence into a number of given families. We introduce the use of fuzzy ARTMAP classifiers and show that coupled with the genetic algorithm based feature subset selection, the system is able to classify protein sequences with an accuracy of 93%. This accuracy is compared with numerous other classification tools and demonstrates that the fuzzy ARTMAP is suitable due to its high accuracy, quick training times and ability for incremental learning.

Published in:

2006 IEEE International Conference on Systems, Man and Cybernetics  (Volume:2 )

Date of Conference:

8-11 Oct. 2006