NDLI: Truncated Profile Hidden Markov Models

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2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology

2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology

Truncated Profile Hidden Markov Models

Content Provider	IEEE Xplore Digital Library
Author	Smith, S.F.
Copyright Year	2005
Description	Author affiliation: Department of Electrical and Computer Engineering Boise State University Boise, Idaho 83725-2075 USA, sfsmith@boisestate.edu (Smith, S.F.)
Abstract	The profile hidden Markov model (HMM) is a powerful method for remote homolog database search. However, evaluating the score of each database sequence against a profile HMM is computationally demanding. The computation time required for score evaluation is proportional to the number of states in the profile HMM. This paper examines whether the number of states can be truncated without reducing the ability of the HMM to find proteins containing members of a protein domain family. A genetic algorithm (GA) is presented which finds a good truncation of the HMM states. The results of using truncation on searches of the yeast, E. coli, and pig genomes for several different protein domain families is shown.
Starting Page	1
Ending Page	5
File Size	123501
Page Count	5
File Format	PDF
ISBN	0780393872
DOI	10.1109/CIBCB.2005.1594926
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Databases Proteins Power engineering computing Genetic algorithms USA Councils Fungi Genomics Bioinformatics Registers
Content Type	Text
Resource Type	Article

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