title: Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification.
abstract: BACKGROUND
  Microarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature of multi-type tumors. Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. The multi-class SVM algorithm of Crammer and Singer provides a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. In this paper, we propose to improve the procedure by imposing shrinkage penalties in learning to enforce solution sparsity.
  
  
  RESULTS
  The original multi-class SVM of Crammer and Singer is effective for multi-class classification but does not conduct variable selection. We improved the method by introducing soft-thresholding type penalties to incorporate variable selection into multi-class classification for high dimensional data. The new methods were applied to simulated data and two cancer gene expression data sets. The results demonstrate that the new methods can select a small number of genes for building accurate multi-class classification rules. Furthermore, the important genes selected by the methods overlap significantly, suggesting general agreement among different variable selection schemes.
  
  
  CONCLUSIONS
  High accuracy and sparsity make the new methods attractive for cancer diagnostics with gene expression data and defining targets of therapeutic intervention.
  
  
  BACKGROUND
  The source MATLAB code are available from http://math.arizona.edu/~hzhang/software.html.
authors: GlaxoSmithKline, Research and Development, Division of Quantitative Sciences, Research Triangle Park, NC 27709, USA. ; Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA. ; Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.
authors: Lingkang
authors: L
authors: Huang
authors: Hao Helen
authors: HH
authors: Zhang
authors: Zhao-Bang
authors: ZB
authors: Zeng
authors: Pierre R
authors: PR
authors: Bushel
keywords: cancer classification; classification; microarray; multi-class SVM; shrinkage methods; support vector machine (SVM); variable selection
references: J Biomed Sci. 2009 Feb 24;16:25
references: 19272192
references: J Comput Biol. 2004;11(2-3):227-42
references: 15285890
references: Jpn J Cancer Res. 2000 Nov;91(11):1103-10
references: 11092974
references: Bioinformatics. 2006 Apr 15;22(8):950-8
references: 16452113
references: Genomics Proteomics Bioinformatics. 2010 Sep;8(3):200-10
references: 20970748
references: Bioinformatics. 2006 Jan 1;22(1):88-95
references: 16249260
references: Science. 1999 Oct 15;286(5439):531-7
references: 10521349
references: Biotechnol Lett. 2005 Apr;27(8):597-603
references: 15973495
references: Cancer Genet Cytogenet. 2002 Jun;135(2):110-9
references: 12127395
references: Nat Med. 2001 Jun;7(6):673-9
references: 11385503