Literature DB >> 9682053

Reduced space hidden Markov model training.

C Tarnas1, R Hughey.   

Abstract

MOTIVATION: Complete forward-backward (Baum-Welch) hidden Markov model training cannot take advantage of the linear space, divide-and-conquer sequence alignment algorithms because of the examination of all possible paths rather than the single best path.
RESULTS: This paper discusses the implementation and performance of checkpoint-based reduced space sequence alignment in the SAM hidden Markov modeling package. Implementation of the checkpoint algorithm reduced memory usage from O(mn) to O (m square root n) with only a 10% slowdown for small m and n, and vast speed-up for the larger values, such as m = n = 2000, that cause excessive paging on a 96 Mbyte workstation. The results are applicable to other types of dynamic programming. AVAILABILITY: A World-Wide Web server, as well as information on obtaining the Sequence Alignment and Modeling (SAM) software suite, can be found at http://www.cse.ucsc. edu/research/compbio/sam.html. CONTACT: rph@cse.ucsc.edu

Mesh:

Year:  1998        PMID: 9682053     DOI: 10.1093/bioinformatics/14.5.401

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  5 in total

1.  Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training.

Authors:  Tin Y Lam; Irmtraud M Meyer
Journal:  Algorithms Mol Biol       Date:  2010-12-09       Impact factor: 1.405

2.  Reticular alignment: a progressive corner-cutting method for multiple sequence alignment.

Authors:  Adrienn Szabó; Adám Novák; István Miklós; Jotun Hein
Journal:  BMC Bioinformatics       Date:  2010-11-23       Impact factor: 3.169

3.  Clustering ionic flow blockade toggles with a mixture of HMMs.

Authors:  Alexander Churbanov; Stephen Winters-Hilt
Journal:  BMC Bioinformatics       Date:  2008-08-12       Impact factor: 3.169

4.  A linear memory algorithm for Baum-Welch training.

Authors:  István Miklós; Irmtraud M Meyer
Journal:  BMC Bioinformatics       Date:  2005-09-19       Impact factor: 3.169

5.  Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory.

Authors:  Alexander Churbanov; Stephen Winters-Hilt
Journal:  BMC Bioinformatics       Date:  2008-04-30       Impact factor: 3.169

  5 in total

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