Abstract
Microarray is a useful technique for measuring expression data of thousands or more of genes simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust gene identification methods is extremely fundamental. Many gene selection methods as well as their corresponding classifiers have been proposed. In the proposed method, a single gene with high class-discrimination capability is selected and classification rules are generated for cancer based on gene expression profiles.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aerman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 1, 6745–6750 (1999)
DeRisi, J., et al.: Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat. Genet. 14(4), 457–460 (1996)
Muralidhar, K., Sarathy, R.: Security of random data perturbation methods. ACM Trans. Database Syst. 24(4), 487–493 (1999)
Petrov, A., Shams, S.: Microarray image processing and quality control. VLSI Signal Processing 38(3), 211–226 (2004)
Su, Y., Murali, T.M., Pavlovic, V., Schaffer, M., Kasif, S.: RankGene: identification of diagnostic genes based on expression data. Bioinformatics 19, 1578–1579 (2003)
Li, L., Weinberg, R.C., Darden, T.A., Pedersen, L.G.: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17, 1131–1142 (2001)
Zhang, H., Yu, C.Y., Singer, B., Xiong, M.: Recursive partitioning for tumor classification with gene expression microarray data. PNAS 98, 6730–6735 (2001)
Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. J. Am. Statistical Assoc. 97(457), 77–87 (2002)
Wang, X., Gotoh, O.: Microarray-Based Cancer Prediction Using Soft Computing Approach. Cancer Informatics 7, 123–139 (2009)
Bradley, P.S., Bennett, K.P., Demiriz, A.: Constrained k-means clustering (Technical Report MSR-TR-2000-65), Microsoft Research, Redmond, WA (2000)
Pensa, R.G., Leschi, C., Besson, J., Boulicaut, J.: Assessment of discretization techniques for relevant pattern discovery from gene expression data. In: 4th Workshop on Data Mining in Bioinformatics (2004)
Peterson, I.: Fuzzy Sets. Science News 144, 55 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pati, S.K., Das, A.K. (2013). Gene Selection and Classification Rule Generation for Microarray Dataset. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31600-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-31600-5_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31599-2
Online ISBN: 978-3-642-31600-5
eBook Packages: EngineeringEngineering (R0)