TY - GEN
T1 - Evaluation of fuzzy measures in profile hidden Markov models for protein sequences
AU - Bidargaddi, Niranjan P.
AU - Chetty, Madhu
AU - Kamruzzaman, Joarder
PY - 2005
Y1 - 2005
N2 - In biological problems such as protein sequence family identification and profile building the additive hypothesis of the probability measure is not well suited for modeling HMM based profiles because of a high degree of interdependency among homologous sequences of the same family. Fuzzy measure theory which is an extension of the classical additive theory is obtained by replacing the additive requirement of classical measures with weaker properties of monotonicity, continuity and semi-continuity. The strong correlations and the sequence preference involved in the protein structures make fuzzy measure architecture based models as suitable candidates for building profiles of a given family since fuzzy measures can handle uncertainties better than classical methods. In this paper we investigate the different measures(S-decomposable, λ and belief measures) of fuzzy measure theory for building profile models of protein sequence problems. The proposed fuzzy measure models have been tested on globin and kinase families. The results obtained from the fuzzy measure models establish the superiority of fuzzy measure theory compared to classical probability measures for biological sequence problems.
AB - In biological problems such as protein sequence family identification and profile building the additive hypothesis of the probability measure is not well suited for modeling HMM based profiles because of a high degree of interdependency among homologous sequences of the same family. Fuzzy measure theory which is an extension of the classical additive theory is obtained by replacing the additive requirement of classical measures with weaker properties of monotonicity, continuity and semi-continuity. The strong correlations and the sequence preference involved in the protein structures make fuzzy measure architecture based models as suitable candidates for building profiles of a given family since fuzzy measures can handle uncertainties better than classical methods. In this paper we investigate the different measures(S-decomposable, λ and belief measures) of fuzzy measure theory for building profile models of protein sequence problems. The proposed fuzzy measure models have been tested on globin and kinase families. The results obtained from the fuzzy measure models establish the superiority of fuzzy measure theory compared to classical probability measures for biological sequence problems.
UR - https://www.scopus.com/pages/publications/33745305584
U2 - 10.1007/11573067_36
DO - 10.1007/11573067_36
M3 - Conference contribution
AN - SCOPUS:33745305584
SN - 3540296743
SN - 9783540296744
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 355
EP - 366
BT - Biological and Medical Data Analysis - 6th International Symposium, ISBMDA 2005, Proceedings
T2 - 6th International Symposium on Biological and Medical Data Analysis, ISBMDA 2005
Y2 - 10 November 2005 through 11 November 2005
ER -