New Hybrid Algorithm for Human Cancer Diseases Classification | ||||
Menoufia Journal of Electronic Engineering Research | ||||
Article 9, Volume 25, Issue 2, July 2016, Page 267-282 | ||||
Document Type: Original Article | ||||
DOI: 10.21608/mjeer.2016.64116 | ||||
View on SCiNiTO | ||||
Authors | ||||
Hanaa Salem1; Gamal Attiya 2; Nawal El-Fishawy2 | ||||
1Communications & Computer Dept., Faculty of Engineering, Delta University, Egypt | ||||
2Dept. of Computer Science and Eng., Faculty of Elect., Eng., Menoufia University | ||||
Abstract | ||||
"> Cancer disease, in any of its forms, represents a major cause of death worldwide. Hence, detecting the cancer disease earlier and classifying the different tumor types is of the greatest importance. Early diagnosis of various tumor types gives better treatment and minimization of toxicity on patients. Accordingly, creating methodologies that able to differentiate efficiently between cancer subtypes is essential. This paper presents a new hybrid methodology to classify Human cancer diseases based on the gene expression profiles. The proposed methodology combines both Information gain (IG) and Deep Genetic Algorithm (DGA). It first uses IG for feature selection, then uses Genetic Algorithm (GA) for feature reduction and finally uses Genetic Programming (GP) for cancer types’ classification. The proposed methodology is evaluated by classifying cancer diseases in seven cancer datasets and the results are compared with that obtained by the most recent approaches. | ||||
References | ||||
x; -webkit-text-size-adjust: auto; -we[1] H. M. Alshamlan, G. H. Badr and Y. A. Alohali, “Genetic bee colony (GBC) algorithm: a new gene selection method for microarray cancer classification”, Computational Biology and Chemistry, Vol. 56, pp.49–60, 2015. [2] E. Bard and W. Hu, “Identification of a 12g signature for lung cancer prognosis through machine learning”, Journal of Cancer Therapy, Vol. 2, Pages 148-156, 2011. [3] J. A. Cruz and D. S. Wishart, “Applications of machine learning in cancer prediction and prognosis”, US National Library of Medicine National Institutes of Health, Cancer Informatics, Vol. 2, pp. 59–77, 2006. [4] R. M. Luque-Baena, D. Urda, J.L. Subirats, L. Franco and J.M. Jerez, “Analysis of cancer microarray data using constructive neural networks and genetic algorithms”, Ignacio Rojas & Francisco M. Ortuño Guzman, ed., 'IWBBIO' , Copicentro Editorial, pp. 55-63, 2013. [5] G. Chakraborty and B. Chakraborty, “Multi-objective optimization using Pareto GA for gene-selection from microarray data for disease classification”, IEEE International Conference Systems, Man, and Cybernetics (SMC), Pages 2629 – 2634, 2013. [6] T. AC, D. Gilbert, “Ensemble machine learning on gene expression data for cancer classification”, Applied Bioinformatics, Vol. 2, pp. 75-83, 2003. [7] T. M. Mitchell “Machine learning”, McGraw-Hill Science/Engineering/Math; New York., March 1997. [8] Salima Omar, Asri Ngadi and Hamid H. Jebur, “Machine learning techniques for anomaly detection: An overview”, International Journal of Computer Applications, Vol. 79, No. 2, pp. 33-41, 2013. [9] H. M. Alshamlan, G. H. Badr, and Y. Alohali, “A study of cancer microarray gene expression profile: objectives and approaches”, Proceedings of the World Congress on Engineering, Vol. 2, pp 1-6, 2013. [10] D. K. S. Yip, I. K Pang and K. Y. Yip, “Systematic exploration of autonomous modules in noisy microRNA-target networks for testing the generality of the ceRNA Hypothesis”, BMC Genomics, Vol. 15, pp. 1178-1190, 2014. [11] M. Khashei, A. Z. Hamadani and M. Bijari, “A fuzzy intelligent approach to the classification problem in gene expression data analysis”, knowledge-Based Systems, Elsevier, Vol. 27, pp. 465–474, 2012. [12] R. M. Luque-Baena, D. Urda, J. L. Subirats, Leonardo Franco and Jose M Jerez, “Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data”, US National Library of MedicineNational Institutes of Health , Theoretical Biology and Medical Modelling, Vol. 11, pp. 1-8, 2014. [13] E. B. Huerta, B. Duval and J. KaoHao, “A hybrid LDA and genetic algorithm for gene selection and classification of microarray data”, El-Sevier Pattern Recognition in Bioinformatics, Vol. 73, Issues 13–15, pp. 2375–2383, 2010. [14] T. Nguyen, A. Khosravi, D. Creighton and S. Nahavandi, “Hidden markov models for cancer classification using gene expression profiles”, Information t-text-size-adjust: auto; -webkit-textSciences, Nature-Inspired Algorithms for Large Scale Global Optimization, El Sevier, Vol. 316, pp. 293–307, 2015. [15] K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction”, Computational and Structural Biotechnology Journal, Elsevier, Vol. 13, pp. 8– 17, 2015. [16] H. Hijazi and Ch. Chan, “A classification framework applied to cancer gene expression profiles”, Journal of healthcare engineering, Vol. 4, pp. 255-283, 2013. [17] A. Choudhary, J. K. Saraswat, “Survey on hybrid approach for feature selection”, International Journal of Science and Research (IJSR), Vol. 3, Issue 4, pp. 438-439, April 2014. [18] S. Patil, G. M. Naik, K. R. Pai, “Survey of microarray data processing for cancer sub classification”, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 2, Pages 110-113, February 2014. [19] J. C. Baez, T. Fritz and T. Leinster “A Characterization of entropy in terms of information loss”, Entropy, Vol. 13, Pages 1945-1957, 2011. [20] L. Chen, K. Wu and Y. Li, “A load balancing algorithm based on maximum entropy methods in homogeneous clusters” , International and Interdisciplinary Open Access Journal of Entropy and Information Studies, Entropy, Vol. 16, Pages 5677-5697, 2014. [21] J. Jeyachidra, M. Punithavalli, “A Comparative analysis of feature selection algorithms on classification of gene microarray dataset”, IEEE, International Conference on Information Communication and Embedded Systems (ICICES), pp. 1088 – 1093, 2013. [22] F. K. Ahmad, S. Deris and N. H. Othman, “Toward integrated clinical and geneexpression profiles for breast cancer prognosis: a review paper”, International Journal of Biometrics and Bioinformatics (IJBB), Vol. 3, Issue 4, pp. 31-47, 2009. [23] C. H. Yang, L. Y. Chuang and C. H. Yang, “IG-GA: a hybrid filter/wrapper method for feature selection of microarray data”, Journal of Medical and Biological Engineering, Vol. 30, pp. 23-28, 2010. [24] D. L. Tong, A. C. Schierz, “Hybrid genetic algorithm-neural network: feature extraction for un-preprocessed microarray data”, Artificial Intelligence in Medicine, El Sevier, Vol. 53, Issue 1, pp. 47–56, 2011. [25] J. H. Holland, “Adaptation in nature and artificial systems”, BOOK, MIT Press Cambridge, MA, USA, and ISBN: 0-262-58111-6, 1992. [26] C. H. Yang, Y. D. Lin, L. Y. Chuang and H. W. Chang, ”Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 10, No. 2, pp. 361-371, 2013. [27] B. S and S. S. Sathya, “A survey of bioinspired optimization algorithms”, International Journal of Soft Computing and Engineering (IJSCE), Vol. 2, Issue 2, Pages 137-151, May 2012. | ||||
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