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Hidden Markov ProcessesTheory and Applications to Biology$
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M. Vidyasagar

Print publication date: 2014

Print ISBN-13: 9780691133157

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691133157.001.0001

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Some Applications to Computational Biology

Some Applications to Computational Biology

Chapter:
(p.225) Chapter Eight Some Applications to Computational Biology
Source:
Hidden Markov Processes
Author(s):

M. Vidyasagar

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691133157.003.0008

This chapter considers some applications of Markov processes and hidden Markov processes to computational biology. It introduces three important problems, namely: sequence alignment, the gene-finding problem, and protein classification. After providing an overview of some relevant aspects of biology, the chapter examines the problem of optimal gapped alignment between two sequences. This is a way to detect similarity between two sequences over a common alphabet, such as the four-symbol alphabet of nucleotides, or the 20-symbol alphabet of amino acids. The chapter proceeds by discussing some widely used algorithms for finding genes from DNA sequences (genomes), including the GLIMMER algorithm and the GENSCAN algorithm. Finally, it describes a special type of hidden Markov model termed profile hidden Markov model, which is commonly used to classify proteins into a small number of groups.

Keywords:   hidden Markov model, Markov process, hidden Markov processes, computational biology, sequence alignment, gene-finding problem, protein classification, optimal gapped alignment, GLIMMER algorithm, GENSCAN algorithm

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