Hidden Markov Models, Theory and Applications
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 . 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.
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