Abstract
DNA microarrays provide an enormous amount of information about genetically conditioned susceptibility to diseases. However, their analysis is uneasy because the number of genes is extremely large with respect to the number of experiments. The problem is that all genes are not essential in gene expression data. Some of the genes may be redundant, and others may be irrelevant and noisy. This research paper studies the gene expression data using rough set theory; it is an intelligent computing method. In this paper, we studied and implemented following discretization methods such as rough discretization (RD), naïve Bayes, max–min, equal width intervals, K-means-based discretization, and entropy-based discretization (EBD) for gene selection using rough set quick reduct (QR) for breast cancer gene expression data. Further, the performance of the above algorithms has been evaluated using classification tools available in Weka software and BPN classifier.
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Acknowledgments
The first author gratefully acknowledges the partial financial assistance under University Research Fellowship, Periyar University, Salem-11, Tamil Nadu, India.
The second author gratefully acknowledges the UGC, New Delhi, for partial financial assistance under UGC-SAP (DRS) Grant No. F.3–50/2011.
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Sathishkumar, E.N., Thangavel, K., Nishama, A. (2014). Comparative Analysis of Discretization Methods for Gene Selection of Breast Cancer Gene Expression Data. In: Krishnan, G., Anitha, R., Lekshmi, R., Kumar, M., Bonato, A., Graña, M. (eds) Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol 246. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1680-3_40
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DOI: https://doi.org/10.1007/978-81-322-1680-3_40
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