International Journal of
Computer Sciences and Engineering

Scholarly Peer-Reviewed Scientific Research Publishing Journal
A survey of Meta-heuristics Approaches for application in Genomic data
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: kunjean4181@gmail.com.

Section:Survey Paper, Product Type: Journal Paper
Volume-5 , Issue-7 , Page no. 51-55, Jul-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i7.5155

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.
           
Abstract :
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
References :
[1] Michael K. K. Leung, Andrew Delong, Babak Alipanahi, and Brendan J. Frey,”Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets”, Proceedings of the IEEE, Vol.104, No.1, January 2016.
[2] E. de Klerk and P. A. C.’t Hoen, ‘‘Alternative mRNA transcription, processing, and translation: Insights from RNA sequencing,’’ Trends Gen., vol. 31, no. 3, pp. 128–139, 2015.
[3] J. Harrow et al., ‘‘GENCODE: The reference human genome annotation for the ENCODE project,’’ Genome Research., vol. 22, no. 9, pp. 1760–1774, 2012.
[4] V. Marx, ‘‘Biology: The big challenges of big data,’’ Nature, vol. 498, no. 7453, pp. 255–260, 2013.
[5] K. Y. Yip, C. Cheng, and M. Gerstein, ‘‘Machine learning and genome annotation: A match meant to be?’’, Genome Biology, vol. 14, no. 5, pp. 205, 2013.
[6] Fred W. Glover and Gary A. Kochenberger, “Handbook of Metaheuristics (International Series in Operations Research & Management Science)”, Springer, January 2003.
[7] Blum C, Roli A, “Metaheuristics in combinatorial optimization: Overview and conceptual comparison”, ACM Computing Survey, vol. 35, no.3, pp.268-308, September 2003.
[8] Glover, F “Future paths for integer programming and links to artificial intelligence”, Computer Operation Research, Vol. 13, pp.533–549, 1986.
[9] Kirkpatrick, S., Gelatt, C., Vecchi, M., “Optimization by simulated annealing”, Science, New Series, vol. 220, No. 4598, pp.671–680, May 1983.
[10] Mladenovic.M, Hansen.P, “Variable neighborhood search”, Computer Operation Research,. Vol.24, pp.1097–1100, 1997.
[11] James Kennedy, Russell Eberhart, “Particle Swarm Optimization”, IEEE International Conference on Neural Networks, Vol. 4, pp. 1942-1948, December 1995.
[12] He, S., Wu, Q.H., Saunders, J.R, “Group search optimizer–an optimization algorithm inspired by animal searching behaviour”, IEEE Transactions on Evolutionary Computer, vol. 13, no.5, pp.973–990, October 2009.
[13] Ajit Kumar, Dharmender Kumar and S.K. Jarial, “A novel hybrid K-means and artificial bee colony algorithm approach for data clustering”, Decision Science Letters, vol. 7, pp. 65-76, April 2017.
[14] Ajit Kumar, Dharmender Kumar and S.K. Jarial, “A Comparative Analysis of Selection Schemes in the Artificial Bee Colony Algorithm”, Computación y Sistemas, vol.20, No.1, pp. 55-66, 2016.
[15] Holland.J.H, “Adaptation in Natural and Artificial Systems”, University of Michigan Press, Ann Arbor, 1975.
[16] Shen, Q.,Wei-Min, S.,Wei, K, “Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data”, Computational Biology and Chemistry, vol. 32, pp. 53–60, 2008.
[17] Neelam Goel, Shailendra Singh and Trilok Chand Aseri, “A comparative analysis of soft computing techniques for gene prediction”, Analytical Biochemistry, vol.438, pp.14-21, March 2013.
[18] Sina Tabakhi, Ali Najafi, Reza Ranjbar and Parham Moradi,” Gene selection for microarray data classification using a novel ant colony optimization”, Neurocomputing, Vol. 168, Issue.C, pp. 1024-1036, May 2015.
[19] C. Gondro, B.P. Kinghorn,” A simple genetic algorithm for multiple sequence alignment”, Genetic and Molecular research. Vol.6, no.5, pp. 964-982, October 2007.
[20] Rasmussen TK and Krink T, “Improved hidden Markov model training for multiple sequence alignment by a particle swarm optimization- evolutionary algorithm hybrid”, Bio systems, vol. 72, pp. 5-17, 2003.
[21] Kamil Kwarciak, Piotr Formanowicz, “Tabu search algorithm for DNA sequencing by hybridization with multiplicity information available”, Elsevier, Computers & Operation Research, Vol.47, pp. 1-10, January2014.
[22] Neethling M and Engelbrecht AP, “Determining RNA Secondary Structure using Set-based Particle Swarm Optimization”, Proc. Of congress on Evolutionary Computation (CEC), IEEE press, USA, 2006.
[23] J.H. Holland,” Adaptation in Natural and Artificial Systems”, the University of Michigan Press, Ann Arbor, Michigan, 1975.
[24] F. Glover, M. Laguna, and R. Marti,” Fundamentals of scatter search and path relinking”, Control and Cybernetics, Vol.39, no.5, pp. 653–684, 2000.
[25] S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, “Optimization by simulated annealing. Science”, Science, Vol. 220, no.4598, pp.671–680, 1983.