Abstract
Understanding the gene expression is an important factor to cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no an obvious exact algorithm that can be implemented individually for various cancer cells. In this paper a research is conducted through the most common top ranked algorithms implemented for cancer gene search and classification, and how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for Bio image analysis for cancer cells and algorithms implemented based on DNA array data. The main purpose of this paper is to explore a road map towards presenting the most current algorithms implemented for cancer gene search and classification.
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References
Shah, S., Kusiak, A.: Cancer gene search with data mining and genetic algorithms. Computers in Biology and Medicine 37(2), 251–261 (2007)
Lee, Z.-J.: An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer. International Journal Artificial Intelligence in Medicine 42, 81–93 (2008)
Liu, H., Liu, L., Zhang, H.: Ensemble gene selection for cancer classifi-cation. Pattern Recognition 43(8), 2763–2772 (2010) ISSN 0031-3203, 10.1016/j.patcog.2010.02.008
Chen, X.-W.: Gene selection for cancer classification using bootstrapped genetic algorithms and support vector machines. In: Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003, August 11-14, pp. 504–505 (2003)
Park, C., Cho, S.-B.: Evolutionary ensemble classifier for lymphoma and colon cancer classification. In: The 2003 Congress on Evolutionary Computation, CEC 2003, December 8-12, vol. 4, pp. 2378–2385 (2003)
Mohamad, M.S., Omatu, S., Yoshioka, M., Deris, S.: An Approach Using Hybrid Methods to Select Informative Genes from Microarray Data for Cancer Classification. In: Second Asia International Conference on Modeling & Simulation, AICMS 2008, May 13-15, pp. 603–608 (2008)
Liu, H., Liu, L., Zhang, H.: Ensemble gene selection for cancer classification. Pattern Recognition 43(8), 2763–2772 (2010) ISSN 0031-3203
Mohamad, M.S., Omatu, S., Deris, S., Hashim, S.Z.M.: A Model for Gene Selection and Classification of Gene Expression Data. International Journal of Artificial Life & Robotics 11(2), 219–222 (2007)
Osareh, A., Shadgar, B.: Microarray data analysis for cancer classification. In: 2010 5th International Symposium on Health Informatics and Bioinformatics (HIBIT), April 20-22, pp. 125–132 (2010)
Nurminen, J.K.: Using software complexity measures to analyze algorithms—an experiment with the shortest-paths algorithms. Computers & Operations Research 30(8), 1121–1134 (2003) ISSN 0305-0548, 10.1016/S0305-0548(02)00060-6
Solorio-Fernandez, S., Martinez-Trinidad, J.F., Carrasco-Ochoa, J.A., Zhang, Y.-Q.: Hybrid feature selection method for biomedical datasets. In: 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), May 9-12, pp. 150–155 (2012)
Wang, L., Chu, F., Xie, W.: Accurate Cancer Classification Using Expres-sions of Very Few Genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(1), 40–53 (2007)
Li, J., Su, H., Chen, H., Futscher, B.W.: Optimal Search-Based Gene Subset Selection for Gene Array Cancer Classification. IEEE Transactions on Information Technology in Biomedicine 11(4), 398–405 (2007)
Yeh, J.-Y., Wu, T.-S., Wu, M.-C., Chang, D.-M.: Applying Data Mining Techniques for Cancer Classification from Gene Expression Data. In: International Conference on Convergence Information Technology, November 21-23, pp. 703–708 (2007)
Reyes-Aldasoro, C.C., Griffiths, M.K., Savas, D., Tozer, G.M.: CAIMAN: An online algorithm repository for Cancer Image Analysis. Computer Methods and Programs in Biomedicine 103(2), 97–103 (2011) ISSN 0169-2607, 10.1016/j.cmpb.2010.07.007
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison Wesley, MA (1989)
Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: ICML, pp. 856–863 (2003)
Tiwari, R., Singh, M.P.: Correlation-based Attribute Selection using Genetic Algorithm. International Journal of Computer Applications (0975 – 8887) 4(8), 28–34 (2010)
Khanesar, M.A., Teshnehlab, M., Shoorehdeli, M.A.: A novel binary particle swarm optimization. In: Mediterranean Conference on Control & Automation, MED 2007, June 27-29, pp. 1–6 (2007)
Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 81–86 (2001)
Gelman, A.: Analysis of Variance - Why it is More Important Than Ever. The Annals of Statistics 33(1), 1–53 (2005)
Vapnik, V.: Statistical learning theory. Wiley (1998)
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Al-Rajab, M.M., Lu, J. (2014). Algorithms Implemented for Cancer Gene Searching and Classifications. In: Basu, M., Pan, Y., Wang, J. (eds) Bioinformatics Research and Applications. ISBRA 2014. Lecture Notes in Computer Science(), vol 8492. Springer, Cham. https://doi.org/10.1007/978-3-319-08171-7_6
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DOI: https://doi.org/10.1007/978-3-319-08171-7_6
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