A clustering approach for estimating parameters of a profile hidden Markov model. (1st January 2013)

• Record Type: Journal Article
• Authors: Aghdam, Rosa; Pezeshk, Hamid; Malekpour, Seyed Amir; Shemehsavar, Soudabeh; Eslahchi, Changiz
• Is Part Of: International journal of data mining and bioinformatics. Volume 8:Number 1(2013)
• Page Start: 66
• Page End: 82
• Publication Date: 2013-01-01
• Subjects: HMMs -- hidden markov models -- PHMM -- profile hidden Markov models -- the Baum-Welch algorithm -- BMCMC -- Bayesian Monte Carlo Markov chain method -- entropy; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.