|A survey of Meta-heuristics Approaches for application in Genomic data|
|Manu Phogat1 , Dharmender Kumar2|
1 Deptt. Of CSE, GJUST, Hisar, India.
2 Deptt. Of CSE, GJUST, Hisar, India.
|Correspondence should be addressed to: firstname.lastname@example.org.|
Section:Survey Paper, Product Type: Journal Paper
Volume-5 , Issue-7 , Page no. 51-55, Jul-2017
Online published on Jul 30, 2017
Copyright © Manu Phogat, Dharmender Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Manu Phogat, Dharmender Kumar, “A survey of Meta-heuristics Approaches for application in Genomic data”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.7, pp.51-55, 2017.
MLA Style Citation: Manu Phogat, Dharmender Kumar "A survey of Meta-heuristics Approaches for application in Genomic data." International Journal of Computer Sciences and Engineering 5.7 (2017): 51-55.
APA Style Citation: Manu Phogat, Dharmender Kumar, (2017). A survey of Meta-heuristics Approaches for application in Genomic data. International Journal of Computer Sciences and Engineering, 5(7), 51-55.
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|the present era is the revolutionary time in genomic applications. In recent years, genomes of various species have been sequenced; genes and proteins have been mapped and learned. Structures of genes and proteins have been implied and their behavior is being understood. Over the past two decades, there is a viable interest in to analysis of gene sequence and microarray data with the help of metaheuristics techniques. Therefore this survey intended to give some nature inspired methods to analyze genomic data such as sequence analysis of various genes, microarray analysis and multiple sequence alignment. The survey later on is followed by the types of main nature inspired algorithms both population and single solution based methods. These are followed by their different application in genomic data and their merits to address specific task.|
|Key-Words / Index Term :|
|Metaheuristics, Microarray, genome, genetic algorithm|
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