Abstract
In this chapter, a new multi-objective blended particle swarm optimization (MOBPSO) technique is proposed for the selection of significant and informative genes from the cancer datasets. As the basic optimization algorithm suffers from the local trapping, a blended Laplacian operator is integrated with it to overcome the drawback. The concept is also implemented for differential evolution, artificial bee colony, genetic algorithm and subsequently multi-objective blended differential evolution (MOBDE), multi-objective blended artificial bee colony (MOBABC) and multi-objective blended genetic algorithm (MOBGA) are proposed to extract the relevant genes from the cancer datasets. Proposed methodology utilizes two objective functions to sort out the genes which are differentially expressed from class to class as well as provides good results for the classification of disease. Experimental result reveals that the proposed methodology very efficiently selects differential and biologically relevant genes which are effective for the classification of disease which in turn offers more useful information about the gene–disease association.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
P. Agarwalla, S. Mukhopadhyay, Selection of relevant genes for pediatric leukemia using co-operative Multiswarm. Mater. Today Proc. 3(10), 3328–3336 (2016)
U. Alon, N. Barkai, D.A. Notterman, K. Gish, S. Ybarra, D. Mack, A.J. Levine, Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 96(12), 6745–6750 (1999)
J. Apolloni, G. LeguizamĂ³n, E. Alba, Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Appl. Soft Comput. 38, 922–932 (2016)
S. Bandyopadhyay, S. Mallik, A. Mukhopadhyay, A survey and comparative study of statistical tests for identifying differential expression from microarray data. IEEE/ACM Trans. Comput. Biol. Bioinform. 11(1), 95–115 (2014)
K.H. Chen, K.J. Wang, M.L. Tsai, K.M. Wang, A.M. Adrian, W.C. Cheng, K.S. Chang, Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm. BMC Bioinform. 15(1), 49 (2014)
M.H. Cheok, W. Yang, C.H. Pui, J.R. Downing, C. Cheng, C.W. Naeve, W.E. Evans, Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells. Nat. Genet. 34(1), 85–90 (2003)
K. Deb, A. Pratap, S. Agarwal, T.A.M.T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, C.D. Bloomfield, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
A.O. Hero, G. Fluery, Pareto-optimal methods for gene filtering. J. Am. Stat. Assoc. (JASA) (2002)
Y. Hippo, H. Taniguchi, S. Tsutsumi, N. Machida, J.M. Chong, M. Fukayama, H. Aburatani, Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Res. 62(1), 233–240 (2002)
D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)
J. Kennedy, Particle swarm optimization. Encyclopedia of Machine Learning (Springer, US, 2011), pp. 760–766
N. Khunlertgit, B.J. Yoon, Identification of robust pathway markers for cancer through rank-based pathway activity inference. Adv. Bioinform. (2013)
S.B. Kotsiantis, I. Zaharakis, P. Pintelas, Supervised machine learning: a review of classification techniques (2007)
L.K. Luo, D.F. Huang, L.J. Ye, Q.F. Zhou, G.F. Shao, H. Peng, Improving the computational efficiency of recursive cluster elimination for gene selection. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(1), 122–129 (2011)
M.S. Mohamad, S. Omatu, S. Deris, M.F. Misman, M. Yoshioka, A multi-objective strategy in genetic algorithms for gene selection of gene expression data. Artif. Life Robot. 13(2), 410–413 (2009)
A. Mukhopadhyay, M. Mandal, Identifying non-redundant gene markers from microarray data: a multiobjective variable length PSO-based approach. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 11(6), 1170–1183 (2014)
K. Price, R.M. Storn, J.A. Lampinen, Â Differential Evolution: A Practical Approach to Global Optimization (Springer Science & Business Media, 2006)
H. Salem, G. Attiya, N. El-Fishawy, Classification of human cancer diseases by gene expression profiles. Appl. Soft Comput. 50, 124–134 (2017)
N. Srinivas, K. Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)
A.V. Ushakov, X. Klimentova, I. Vasilyev, Bi-level and bi-objective p-median type problems for integrative clustering: application to analysis of cancer gene-expression and drug-response data. IEEE/ACM Trans. Comput. Biol. Bioinform. (2016)
J. Yang, V. Honavar, Feature subset selection using a genetic algorithm. IEEE Intell. Syst. Appl. 13(2), 44–49 (1998)
L. Zhang, J. Kuljis, X. Liu, Information visualization for DNA microarray data analysis: a critical review. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(1), 42–54 (2008)
Q. Zhang, H. Li, MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
C.H. Zheng, W. Yang, Y.W. Chong, J.F. Xia, Identification of mutated driver pathways in cancer using a multi-objective optimization model. Comput. Biol. Med. 72, 22–29 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Agarwalla, P., Mukhopadhyay, S. (2018). Feature Selection Using Multi-Objective Optimization Technique for Supervised Cancer Classification. In: Mandal, J., Mukhopadhyay, S., Dutta, P. (eds) Multi-Objective Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-13-1471-1_9
Download citation
DOI: https://doi.org/10.1007/978-981-13-1471-1_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1470-4
Online ISBN: 978-981-13-1471-1
eBook Packages: Computer ScienceComputer Science (R0)