The Block Hidden Markov Model for Biological Sequence Analysis

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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.

Original languageEnglish
Title of host publicationKnowledge-BasedIntelligent Informationand Engineering Systems : 8th International Conference, KES 2004, Proceedings
EditorsMircea Gh. Negoita, Robert J. Howlett, Lakhmi Jain
Number of pages7
Volume1
PublisherSpringer
Publication date2004
Pages64-70
ISBN (Print)3-540-23318-0, 978-3-540-23318-3
ISBN (Electronic)978-3-540-30132-5
DOIs
Publication statusPublished - 2004
Event8th International conference, KES 2004 - Wellington, New Zealand
Duration: 20 Sep 200425 Sep 2004

Conference

Conference8th International conference, KES 2004
LandNew Zealand
ByWellington
Periode20/09/200425/09/2004
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3213
ISSN0302-9743

ID: 249813111