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EURASIP Journal on Bioinformatics and Systems Biology
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Gene Selection for Multiclass Prediction by Weighted Fisher Criterion

EURASIP Journal on Bioinformatics and Systems Biology volume 2007, Article number: 64628 (2007) Cite this article

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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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, 22203, USA

    Jianhua Xuan, Yue Wang, Yibin Dong, Yuanjian Feng, Bin Wang & Zuyi Wang

  2. Department of Pediatric Oncology, National Cancer Institute, Gaithersburg, MD, 20877, USA

    Javed Khan

  3. Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC, 20010, USA

    Maria Bakay, Zuyi Wang, Yi-Wen Chen & Eric Hoffman

  4. Disease Pathogenesis Program, Children's Memorial Research Center, Chicago, IL, 60614, USA

    Lauren Pachman

  5. Department of Biological Chemistry, University of California, Irvine, CA, 92697, USA

    Sara Winokur

  6. Lombardi Cancer Center, Georgetown University, Washington, DC, 20007, USA

    Robert Clarke

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  1. Jianhua Xuan
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  2. Yue Wang
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  3. Yibin Dong
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  7. Maria Bakay
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Correspondence to Jianhua Xuan.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Xuan, J., Wang, Y., Dong, Y. et al. Gene Selection for Multiclass Prediction by Weighted Fisher Criterion. J Bioinform Sys Biology 2007, 64628 (2007). https://doi.org/10.1155/2007/64628

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  • DOI: https://doi.org/10.1155/2007/64628

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