A clustering approach for estimating parameters of a profile hidden Markov model

作者：Aghdam Rosa; Pezeshk Hamid^*; Malekpour Seyed Amir; Shemehsavar Soudabeh; Eslahchi Changiz

来源：International Journal of Data Mining and Bioinformatics, 2013, 8(1): 66-82.

DOI：10.1504/IJDMB.2013.054696

• HMMs
• hidden markov models
• PHMM
• profile hidden Markov models
• the Baum-Welch algorithm
• BMCMC
• Bayesian Monte Carlo Markov chain method
• entropy

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.

• 出版日期2013
• 单位上海生物信息技术研究中心