The Block Hidden Markov Model for Biological Sequence Analysis
Won, Kyoung-Jae, Prugel-Bennett, Adam and Krogh, Anders, Mircea Gh, Mircea Gh, Howlett, Robert J. and Jain, Lakhmi C. (eds.) (2004) The Block Hidden Markov Model for Biological Sequence Analysis. Lecture Notes in Artificial Intelligence, Knowledge-Based Intelligent Information and Engineering Systems: 8th International Conference, KES 2004, Wellington, New Zealand, September 20-25, 2004, 3213, 64-70.
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The Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimising the structure of HMMs would be highly desirable. To maintain biologically interpretable blocks inside the HMM, we used a Genetic Algorithm (GA) that has HMM blocks in its coding representation. We developed special genetics operations that maintain the useful HMM blocks. To prevent over-fitting a separate data set is used for comparing the performance of the HMMs to that used for the Baum-Welch training. The performance of this algorithm is applied to finding HMM structures for the promoter and coding region of C. jejuni. The GA-HMM was capable of finding a superior HMM to a hand-coded HMM designed for the same task which has been published in the literature.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||30 Mar 2005|
|Last Modified:||18 Aug 2012 03:58|
|Contributors:||Won, Kyoung-Jae (Author)
Prugel-Bennett, Adam (Author)
Krogh, Anders (Author)
Mircea Gh, Mircea Gh (Editor)
Howlett, Robert J. (Editor)
Jain, Lakhmi C. (Editor)
|Further Information:||Google Scholar|
|ISI Citation Count:||2|
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