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Performance evaluation of Warshall algorithm and dynamic programming for Markov chain in local sequence alignment

Content Provider	Springer Nature Link
Author	Khan, Mohammad Ibrahim kamal, Md. Sarwar
Copyright Year	2015
Abstract	Markov Chain is very effective in prediction basically in long data set. In DNA sequencing it is always very important to find the existence of certain nucleotides based on the previous history of the data set. We imposed the Chapman Kolmogorov equation to accomplish the task of Markov Chain. Chapman Kolmogorov equation is the key to help the address the proper places of the DNA chain and this is very powerful tools in mathematics as well as in any other prediction based research. It incorporates the score of DNA sequences calculated by various techniques. Our research utilize the fundamentals of Warshall Algorithm (WA) and Dynamic Programming (DP) to measures the score of DNA segments. The outcomes of the experiment are that Warshall Algorithm is good for small DNA sequences on the other hand Dynamic Programming are good for long DNA sequences. On the top of above findings, it is very important to measure the risk factors of local sequencing during the matching of local sequence alignments whatever the length.
Starting Page	78
Ending Page	81
Page Count	4
File Format	PDF
ISSN	19132751
Journal	Interdisciplinary Sciences: Computational Life Sciences
Volume Number	7
Issue Number	1
e-ISSN	18671462
Language	English
Publisher	International Association of Scientists in the Interdisciplinary Areas
Publisher Date	2014-08-09
Publisher Place	Quebec
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hidden Markov model Chapman-Kolmogorov formula Warshall algorithm Dynamic Programming Score measurement Computer Application in Life Sciences Computational Biology/Bioinformatics Statistics for Life Sciences, Medicine, Health Sciences Theoretical and Computational Chemistry Theoretical, Mathematical and Computational Physics Computational Science and Engineering
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Health Informatics Computer Science Applications

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