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Selecting marker genes for cancer classification using supervised weighted kernel clustering and the support vector machine

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  • Shim, Jooyong
  • Sohn, Insuk
  • Kim, Sujong
  • Lee, Jae Won
  • Green, Paul E.
  • Hwang, Changha

Abstract

Due to recent interest in the analysis of DNA microarray data, new methods have been considered and developed in the area of statistical classification. In particular, according to the gene expression profile of existing data, the goal is to classify the sample into a relevant diagnostic category. However, when classifying outcomes into certain cancer types, it is often the case that some genes are not important, while some genes are more important than others. A novel algorithm is presented for selecting such relevant genes referred to as marker genes for cancer classification. This algorithm is based on the Support Vector Machine (SVM) and Supervised Weighted Kernel Clustering (SWKC). To investigate the performance of this algorithm, the methods were applied to a simulated data set and some real data sets. For comparison, some other well-known methods such as Prediction Analysis of Microarrays (PAM), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and a Structured Polychotomous Machine (SPM) were considered. The experimental results indicate that the proposed SWKC/SVM algorithm is conceptually much simpler and performs more efficiently than other existing methods used in identifying marker genes for cancer classification. Furthermore, the SWKC/SVM algorithm has the advantage that it requires much less computing time compared with the other existing methods.

Suggested Citation

  • Shim, Jooyong & Sohn, Insuk & Kim, Sujong & Lee, Jae Won & Green, Paul E. & Hwang, Changha, 2009. "Selecting marker genes for cancer classification using supervised weighted kernel clustering and the support vector machine," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1736-1742, March.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:5:p:1736-1742
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    References listed on IDEAS

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    1. Park, Changyi & Koo, Ja-Yong & Kim, Sujong & Sohn, Insuk & Lee, Jae Won, 2008. "Classification of gene functions using support vector machine for time-course gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2578-2587, January.
    2. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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    Cited by:

    1. Insuk Sohn & Jooyong Shim & Changha Hwang & Sujong Kim & Jae Won Lee, 2014. "Transcription factor-binding site identification and gene classification via fusion of the supervised-weighted discrete kernel clustering and support vector machine," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 573-581, March.
    2. Wenyan Zhong & Jingjing Wu, 2017. "Feature Selection for Cancer Classification Using Microarray Gene Expression Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 1(2), pages 33-39, April.
    3. Ramos, Sandra & Amaral Turkman, Antónia & Antunes, Marília, 2010. "Bayesian classification for bivariate normal gene expression," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 2012-2020, August.
    4. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.

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