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Paper   IPM / Biological / 13290
School of Biological Sciences
  Title:   A Clustering Approach for Estimating Parameters of a Profle Hidden Markov Model
  Author(s): 
1 . R. Aghdam.
2 . H. Pezeshk.
3 . S.A. Malekpour.
4 . S. Shemehsavar.
5 . M. Sadeghi.
6 . C. Eslahchi.
  Status:   Published
  Journal: International Journal of Data Mining and Bioinformatics
  No.:  1
  Vol.:  8
  Year:  2012
  Pages:   66-82
  Supported by:  IPM
  Abstract:
A Profile Hidden Markov Model (PHMM) is a standard form of a Hidden Markov Models used for modelling 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.

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