Hidden Markov models incorporating fuzzy measures and integrals for protein sequence identification and alignment.
Profile hidden Markov models (HMMs) based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein sequences belonging to a given family. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures and Choquet integrals, thus further extends the generalized HMM. Based on the fuzzified forward and backward variables, we propose a fuzzy Baum-Welch parameter estimation algorithm for profiles. The strong correlations and the sequence preference involved in the protein structures make this fuzzy architecture based model as a suitable candidate for building profiles of a given family, since the fuzzy set can handle uncertainties better than classical methods.
Bidargaddi
Niranjan P
NP
Gippsland School of Computing and Information Technology, Monash University, Churchill, VIC 3842, Australia.
Chetty
Madhu
M
Kamruzzaman
Joarder
J
D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D030562:Databases, Protein; D017143:Fuzzy Logic; D005914:Globins / Q000737:chemistry / Q000235:genetics; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D011338:Probability Theory; D011494:Protein Kinases / Q000737:chemistry / Q000235:genetics; D016415:Sequence Alignment / Q000706:statistics & numerical data*; D020539:Sequence Analysis, Protein / Q000706:statistics & numerical data*
D005914:Globins; D011494:Protein Kinases