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Hidden Markov Models, Theory and Applications

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Hidden Markov models (HMMs) are well developed statistical models to capture hidden information from observable sequential symbols. They were first used in speech recognition in 1970s and have been successfully applied to the analysis of biological sequences since late 1980s as in finding protein secondary structure, CpG islands and families of related DNA or protein sequences [1]. In a HMM, the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the observable parameters. In this chapter, we described two applications using HMMs to predict gene functions in yeast and DNA copy number alternations in human tumor cells, based on gene expression microarray data.


Chapter 12 in "Hidden Markov Models, Theory and Applications", book edited by Przemyslaw Dymarski, ISBN 978-953-307-208-1, Published: April 19, 2011 under CC BY-NC-SA 3.0 license. © The Author(s).