“School of Biological”
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| Paper IPM / Biological / 13477 |
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| Abstract: | |||||||
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Hidden Markov Models (HMMs) have been extensively used in biological sequence analysis. They are applied to protein sequence alignment, protein family annotation and gene-finding. Profile Hidden Markov Models (PHMMs) are HMMs with specific architecture that is suitable for modeling sequence profiles. It is possible to train the PHMM directly from unaligned sequences. Estimating parameters of PHMM is a challenging task in bioinformatics. The Baum-Welch algorithm and the Bayesian Monte Carlo Markov Chain (BMCMC) method are well studied approaches in this field. In this paper, we first implement two methods for estimating parameters of small artificial PHMM with 6 Match states and mid entropy. Secondly, in order to improve the prediction accuracy of the estimation of the parameters of the PHMM, we classify the training data based on their phylogenetic tree. We finally apply an heuristic algorithm for estimating parameters of the PHMM. We believe that using our methodology improves the precision of the estimation considerably. Using our approach, in which a combination of classification and estimation, in this work, it is concluded that the BMCMC method performs better than the Maximum Likelihood estimation.
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