Mapping of DNA sequences using hidden Markov model self organizing maps

2013 ◽  
Author(s):  
Hiroshi Dozono ◽  
Gen Niina
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Dna Sequences ◽  
Hidden Markov ◽  
Self Organizing Maps ◽  
Self Organizing

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Self-Organizing Hidden Markov Model Map (SOHMMM)

2013 ◽  
Vol 48 ◽  
pp. 133-147 ◽  
Author(s):  
Christos Ferles ◽  
Andreas Stafylopatis
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing

Some Applications to Computational Biology

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Amino Acids ◽  
Computational Biology ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Markov Processes ◽  
Sequence Alignment ◽  
Dna Sequences ◽  
Hidden Markov ◽  
Gapped Alignment ◽  
Hidden Markov Processes

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.

scholarly journals Automatic extraction of motifs represented in the hidden Markov model from a number of DNA sequences

1998 ◽  
Vol 14 (4) ◽  
pp. 317-325 ◽  
Author(s):  
T. Yada ◽  
Y. Totoki ◽  
M. Ishikawa ◽  
K. Asai ◽  
K. Nakai
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Dna Sequences ◽  
Hidden Markov ◽  
Automatic Extraction
Sign in / Sign up

Export Citation Format

Share Document