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Paper   IPM / Biological / 13213
School of Biological Sciences
  Title:   A Bidirectional Hidden Markov Model In Linear Memory
1.  N. Ejlali.
2.  H. Pezeshk.
  Status:   Published
  Journal: Journal of Statistical Science
  No.:  2
  Vol.:  2
  Year:  2010
  Pages:   131-148
  Supported by:  IPM
Hidden Markov models are widely used in Bioinformatics. They are applied to protein sequence alignment, protein family annotation and gene-finding.The Baum-Welch training is an expectation-maximization algorithm for training the emission and transition probabilities of hidden Markov models. For very long training sequence, even the most efficient algorithms are memory-consuming. In this paper we discuss different approaches to decrease the memory use and compare the performance of different algorithms. In addition, we propose a bidirection algorithm with linear memory. We apply this algorithm to simulated data of protein profile to analyze the strength and weakness of the algorithm.

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