A fuzzy viterbi algorithm for improved sequence alignment and searching of proteins

Authors:
N. P. Bidargaddi;M Chetty;J Kamruzzaman
Affiliations:
Gippsland School of Computing and Information Technology, Monash University, Vic, Australia;Gippsland School of Computing and Information Technology, Monash University, Vic, Australia;Gippsland School of Computing and Information Technology, Monash University, Vic, Australia
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

Citing 1
Cited 1

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Evaluation of fuzzy measures in profile hidden markov models for protein sequences

ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis

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Abstract

Profile HMMs based on classical hidden Markov models have been widely studied for identification of members belonging to protein sequence families. Classical Viterbi search algorithm which has been used traditionally to calculate log-odd scores of the alignment of a new sequence to a profile model is based on the probability theory. To overcome the limitations of the classical HMM and for achieving an improved alignment and better log-odd scores for the sequences belonging to a given family, we propose a fuzzy Viterbi search algorithm which is based on Choquet integrals and Sugeno fuzzy measures. The proposed search algorithm incorporates ascending values of the scores of the neighboring states while calculating the scores for a given state, hence providing better alignment and improved log-odd scores. The proposed fuzzy Viterbi algorithm for profiles along with classical Viterbi search algorithm has been tested on globin and kinase families. The results obtained in terms of log-odd scores, Z-scores and other statistical analysis establish the superiority of fuzzy Viterbi search algorithm.