Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions

2006 ◽  
Vol 101 (476) ◽  
pp. 1323-1332 ◽  
Author(s):  
Ming Yuan ◽  
Christina Kendziorski
Keyword(s):  
Hidden Markov Models ◽  
Time Course ◽  
Markov Models ◽  
Hidden Markov ◽  
Microarray Time Course ◽  
Time Course Data

scholarly journals Using hidden Markov models to analyze gene expression time course data

2003 ◽  
Vol 19 (Suppl 1) ◽  
pp. i255-i263 ◽  
Author(s):  
A. Schliep ◽  
A. Schonhuth ◽  
C. Steinhoff
Keyword(s):  
Gene Expression ◽  
Hidden Markov Models ◽  
Time Course ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Course Data ◽  
Expression Time

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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