Document Type
Article
Publication Date
4-19-2011
Publication Title
Hidden Markov Models, Theory and Applications
First Page
249
Last Page
268
Abstract
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.
Recommended Citation
Huimin Geng, Xutao Deng and Hesham H Ali (2011). Applications of Hidden Markov Models in Microarray Gene Expression Data, Hidden Markov Models, Theory and Applications, Dr. Przemyslaw Dymarski (Ed.), InTech, DOI: 10.5772/15194. Available from: https://www.intechopen.com/books/hidden-markov-models-theory-and-applications/applications-of-hidden-markov-models-in-microarray-gene-expression-data
Comments
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).