Summary
Microarray technology allows to measureing the expression levels of thousands of genes in an experiment. This technology required requires computational solutions capable of dealing with great amounts of data and as well as techniques to explore the data and extract knowledge which allow patients classification. This paper presents a systems based on Case-based reasoning (CBR) for automatic classification of leukemia patients from microarray data. The system incorporates novel algorithms for data mining that allow to filter and classify as well as extraction of knowledge. The system has been tested and the results obtained are presented in this paper.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Shortliffe, E., Cimino, J.: Biomedical Informatics: Computer Applications in Health Care and Biomedicine. Springer, Heidelberg (2006)
Tsoka, S., Ouzounis, C.: Recent developments and future directions in computational genomics. FEBS Letters 480(1), 42–48 (2000)
Lander, E., et al.: Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001)
Rubnitz, J., Hijiya, N., Zhou, Y., Hancock, M., Rivera, G., Pui, C.: Lack of benefit of early detection of relapse after completion of therapy for acute lymphoblastic leukemia. Pediatric Blood & Cancer 44(2), 138–141 (2005)
Armstrong, N., van de Wiel, M.: Microarray data analysis: From hypotheses to conclusions using gene expression data. Cellular Oncology 26(5-6), 279–290 (2004)
Quackenbush, J.: Computational analysis of microarray data. Nature Review Genetics 2(6), 418–427 (2001)
Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)
Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y., Antonellis, K., Scherf, U., Speed, T.: Exploration, Normalization, and Summaries of High density Oligonucleotide Array Probe Level Data. Biostatistics 4, 249–264 (2003)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics, 59–69 (1982)
Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D., Leen, T. (eds.), Advances in Neural Information Processing Systems, vol. 7, pp. 625–632, Cambridge (1995)
Martinetz, T.: Competitive Hebbian learning rule forms perfectly topology preserving maps. In: ICANN 1993: International Conference on Artificial Neural Networks, pp. 427–434. Springer, Heidelberg (1993)
Martinetz, T., Schulten, K.: A neural-gas network learns topologies. In: Kohonen, T., Makisara, K., Simula, O., Kangas, J. (eds.) Artificial Neural Networks, Amsterdam, pp. 397–402 (1991)
Brunelli, R.: Histogram Analysis for Image Retrieval. Pattern Recognition 34, 1625–1637 (2001)
Jolliffe, I.: Principal Component Analysis, 2nd edn. Series in Statistics. Springer, Heidelberg (2002)
Riverola, F., Daz, F., Corchado, J.: Gene-CBR: a case-based reasoning tool for cancer diagnosis using microarray datasets. Computational Intelligence 22(3-4), 254–268 (2006)
Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
Saitou, N., Nie, M.: The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4, 406–425 (1987)
Sneath, P., Sokal, R.: Numerical Taxonomy. The Principles and Practice of Numerical Classification. W.H. Freeman Company, San Francisco (1973)
Breiman, L., Friedman, J., Olshen, A., Stone, C.: Classification and regression trees. Wadsworth International Group, Belmont (1984)
Quinlan, J.: Discovering rules by induction from large collections of examples. In: Michie, D. (ed.) Expert systems in the micro electronic age, pp. 168–201. Edinburgh University Press, Edinburgh (1979)
Holder, D., Raubertas, R., Pikounis, V., Svetnik, V., Soper, K.: Statistical analysis of high density oligonucleotide arrays: a SAFER approach. In: Proceedings of the ASA Annual Meeting Atlanta, GA (2001)
Corchado, J., Corchado, E., Aiken, J., Fyfe, C., Fdez-Riverola, F., Glez-Bedia, M.: Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems. In: Proceedings. of the 5th International Conference on Case-Based Reasoning, pp. 107–121 (2003)
Quackenbush, J.: Microarray Analysis and Tumor Classification. The new england journal o f medicine, 2463–2472 (2006)
Zhenyu, C., Jianping, L., Liwei, W.: A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue. Artificial Intelligence in Medicine 41, 161–175 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rodríguez, S., De Paz, J.F., Bajo, J., Corchado, J.M. (2009). Applying CBR Systems to Micro Array Data Classification. In: Corchado, J.M., De Paz, J.F., Rocha, M.P., Fernández Riverola, F. (eds) 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). Advances in Soft Computing, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85861-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-85861-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85860-7
Online ISBN: 978-3-540-85861-4
eBook Packages: EngineeringEngineering (R0)