title: A clustering approach for estimating parameters of a profile hidden Markov model.
abstract: A Profile Hidden Markov Model (PHMM) is a standard form of a Hidden Markov Models used for modeling protein and DNA sequence families based on multiple alignment. In this paper, we implement Baum-Welch algorithm and the Bayesian Monte Carlo Markov Chain (BMCMC) method for estimating parameters of small artificial PHMM. In order to improve the prediction accuracy of the estimation of the parameters of the PHMM, we classify the training data using the weighted values of sequences in the PHMM then apply an algorithm for estimating parameters of the PHMM. The results show that the BMCMC method performs better than the Maximum Likelihood estimation.
authors.affiliation: School of Mathematics, Statistics and Computer Science, Shahid Beheshti University, G.C., Tehran, Iran. rosaaghdam@gmail.com
authors.forename: Rosa
authors.initials: R
authors.lastname: Aghdam
authors.forename: Hamid
authors.initials: H
authors.lastname: Pezeshk
authors.forename: Seyed Amir
authors.initials: SA
authors.lastname: Malekpour
authors.forename: Soudabeh
authors.initials: S
authors.lastname: Shemehsavar
authors.forename: Changiz
authors.initials: C
authors.lastname: Eslahchi
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001499:Bayes Theorem; D016000:Cluster Analysis; D016013:Likelihood Functions; D008390:Markov Chains; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D020539:Sequence Analysis, Protein