Quality assessment of gene selection in microarray data

Authors:
C. H. Park;M. Jeon;P. Pardalos;H. Park
Affiliations:
Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea;School of Information and Mechatronics, Gwangju Institute of Science and Technology, Gwangju, Korea;Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA;College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
Venue:
Optimization Methods & Software - Systems Analysis, Optimization and Data Mining in Biomedicine
Year:
2007

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Abstract

In microarray data, gene selection can make data analysis efficient and biological interpretations of the selected genes can be very useful. However, microarray data have typically several thousands of genes but only tens of samples, referred to as a small sample-size problem. In this paper, we discuss some problems on gene selection that can occur owing to a small sample size: whether gene selection relying on the extremely small number of samples is reliable and meaningful. Experimental comparisons of well-known three gene selection methods show that classification performances can be very sensitive to training samples and preprocessing steps. We also measure consistency in gene ranking under the changes of training samples or different selection criteria.