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EURASIP J Bioinform Syst Biol. 2007; 2007(1): 64628.
Published online 2007 July 10. doi:  10.1155/2007/64628
PMCID: PMC3171347
Copyright ©2007 Jianhua Xuan et al.
Gene Selection for Multiclass Prediction by Weighted Fisher Criterion
Jianhua Xuan,corresponding author1 Yue Wang,1 Yibin Dong,1 Yuanjian Feng,1 Bin Wang,1 Javed Khan,2 Maria Bakay,3 Zuyi Wang,1,3 Lauren Pachman,4 Sara Winokur,5 Yi-Wen Chen,3 Robert Clarke,6 and Eric Hoffman3
1Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
2Department of Pediatric Oncology, National Cancer Institute, Gaithersburg, MD 20877, USA
3Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC 20010, USA
4Disease Pathogenesis Program, Children's Memorial Research Center, Chicago, IL 60614, USA
5Department of Biological Chemistry, University of California, Irvine, CA 92697, USA
6Lombardi Cancer Center, Georgetown University, Washington, DC 20007, USA
corresponding authorCorresponding author.
Jianhua Xuan: xuan/at/vt.edu; Yue Wang: yuewang/at/vt.edu; Yibin Dong: yibin.dong/at/vt.edu; Yuanjian Feng: yjfeng/at/vt.edu; Bin Wang: binwang/at/vt.edu; Javed Khan: khanjav/at/mail.nih.gov; Maria Bakay: mbakay/at/cnmcresearch.org; Zuyi Wang: zwang/at/cnmcresearch.org; Lauren Pachman: pachman/at/northwestern.edu; Sara Winokur: stwinoku/at/uci.edu; Yi-Wen Chen: ychen/at/cnmcresearch.org; Robert Clarke: clarker/at/georgetown.edu; Eric Hoffman: ehoffman/at/cnmcresearch.org
Received August 30, 2006; Revised December 16, 2006; Accepted March 20, 2007.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step, individually discriminatory genes (IDGs) are identified by using one-dimensional weighted Fisher criterion (wFC). In the second step, jointly discriminatory genes (JDGs) are selected by sequential search methods, based on their joint class separability measured by multidimensional weighted Fisher criterion (wFC). The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks (ANNs) and/or support vector machines (SVMs). By applying the proposed IDG/JDG approach to two microarray studies, that is, small round blue cell tumors (SRBCTs) and muscular dystrophies (MDs), we successfully identified a much smaller yet efficient set of JDGs for diagnosing SRBCTs and MDs with high prediction accuracies (96.9% for SRBCTs and 92.3% for MDs, resp.). These experimental results demonstrated that the two-step gene selection method is able to identify a subset of highly discriminative genes for improved multiclass prediction.
 
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