title: Simple and flexible classification of gene expression microarrays via Swirls and Ripples.
abstract: BACKGROUND
  A simple classification rule with few genes and parameters is desirable when applying a classification rule to new data. One popular simple classification rule, diagonal discriminant analysis, yields linear or curved classification boundaries, called Ripples, that are optimal when gene expression levels are normally distributed with the appropriate variance, but may yield poor classification in other situations.
  
  
  RESULTS
  A simple modification of diagonal discriminant analysis yields smooth highly nonlinear classification boundaries, called Swirls, that sometimes outperforms Ripples. In particular, if the data are normally distributed with different variances in each class, Swirls substantially outperforms Ripples when using a pooled variance to reduce the number of parameters. The proposed classification rule for two classes selects either Swirls or Ripples after parsimoniously selecting the number of genes and distance measures. Applications to five cancer microarray data sets identified predictive genes related to the tissue organization theory of carcinogenesis.
  
  
  CONCLUSIONS
  The parsimonious selection of classifiers coupled with the selection of either Swirls or Ripples provides a good basis for formulating a simple, yet flexible, classification rule. Open source software is available for download.
authors.affiliation: Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, EPN 3131, 6130 Executive Blvd MSC 7354, Bethesda, MD 20892-7354, USA. sb16i@nih.gov
authors.forename: Stuart G
authors.initials: SG
authors.lastname: Baker
mesh_terms: D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D012984:Software
references.citation: Clin Cancer Res. 2005 Apr 1;11(7):2576-82
references.pmid: 15814636
references.citation: Cancer Cell. 2002 Mar;1(2):133-43
references.pmid: 12086872
references.citation: Nature. 2002 Jan 24;415(6870):436-42
references.pmid: 11807556
references.citation: Lancet. 2005 Feb 5-11;365(9458):488-92
references.pmid: 15705458
references.citation: Mol Biol Cell. 2009 Jul;20(13):3115-24
references.pmid: 19439447
references.citation: J Clin Oncol. 2010 Jul 10;28(20):3215-8
references.pmid: 20547997
references.citation: Cancer Res. 1973 Dec;33(12):3139-54
references.pmid: 4357350
references.citation: Science. 1999 Oct 15;286(5439):531-7
references.pmid: 10521349
references.citation: Proc Natl Acad Sci U S A. 1999 Jun 8;96(12):6745-50
references.pmid: 10359783
references.citation: Semin Cancer Biol. 2008 Oct;18(5):372-7
references.pmid: 18472276
references.citation: Bioinformatics. 2005 Nov 15;21(22):4148-54
references.pmid: 16174683
references.citation: Cancer Res. 1997 Jul 15;57(14):2884-7
references.pmid: 9230195
references.citation: Cancer Cell. 2002 Mar;1(2):203-9
references.pmid: 12086878
references.citation: BMC Cancer. 2007 Aug 06;7:151
references.pmid: 17683619
references.citation: J R Stat Soc Ser A Stat Soc. 2009 Oct 1;172(4):729-748
references.pmid: 20069131
references.citation: BMC Bioinformatics. 2010 May 20;11:271
references.pmid: 20487548
references.citation: Biostatistics. 2007 Jan;8(1):86-100
references.pmid: 16603682
references.citation: BMC Cancer. 2009 Mar 23;9:89
references.pmid: 19309499
references.citation: BMC Bioinformatics. 2006 Sep 07;7:407
references.pmid: 16959042
references.citation: J Natl Cancer Inst. 2009 Nov 18;101(22):1538-42
references.pmid: 19843888