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.