Nonlinear probit gene classification using mutual information and wavelet-based feature selection Academic Article

abstract

• We consider the problem of cancer classification from gene expression data. We propose using a mutual information-based gene or feature selection method where features are wavelet-based. The bootstrap technique is employed to obtain an accurate estimate of the mutual information. We then develop a nonlinear probit Bayesian classifier consisting of a linear term plus a nonlinear term, the parameters of which are estimated using the Gibbs sampler. These new methods are applied to analyze breast-cancer data and leukemia data. The results indicate that the proposed gene and feature selection method is very accurate in breast-cancer and leukemia classifications.

authors

• Dougherty, Edward

published proceedings

• JOURNAL OF BIOLOGICAL SYSTEMS

author list (cited authors)

• Zhou, X. B., Wang, X. D., & Dougherty, E. R.

citation count

• 37

complete list of authors

• Zhou, XB||Wang, XD||Dougherty, ER

publication date

• September 2004

publisher

• World Scientific Publishing  Publisher

published in

• Journal of Biological Systems  Journal

keywords

• Gene Microarray
• Gene Selection
• Gibbs Sampler
• Mutual Information
• Nonlinear Probit Classifier
• Wavelet Transformation

Digital Object Identifier (DOI)

• 10.1142/S0218339004001178

start page

• 371

end page

• 386

volume

• 12

issue

• 3

URL

• http%3A%2F%2Fdx.doi.org%2F10.1142%2Fs0218339004001178