title: Selecting dissimilar genes for multi-class classification, an application in cancer subtyping.
abstract: BACKGROUND
  Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker identification, which are expected to be closely related to the disease. A most important task of these identified genes is that they can be used to construct a classifier which can effectively diagnose disease and even recognize the disease subtypes. Binary classification, for example, diseased or healthy, in microarray data analysis has been successful, while multi-class classification, such as cancer subtyping, remains challenging.
  
  
  RESULTS
  We target on the challenging multi-class classification in microarray data analysis, especially on the cancer subtyping using gene expression microarray. We present a novel class discrimination strength vector to represent individual genes and introduce a new measurement to quantify the class discrimination strength difference between two genes. Such a new distance measure is employed in gene clustering, and subsequently the gene cluster information is exploited to select a set of genes which can be used to construct a sample classifier. We tested our method on four real cancer microarray datasets each contains multiple subtypes of cancer patients. The experimental results show that the constructed classifiers all achieved a higher classification accuracy than the previously best classification results obtained on these four datasets. Additional tests show that the selected genes by our method are less correlated and they all contribute statistically significantly to the more accurate cancer subtyping.
  
  
  CONCLUSIONS
  The proposed novel class discrimination strength vector is a better representation than the gene expression vector, in the sense that it can be used to effectively eliminate highly correlated but redundant genes for classifier construction. Such a method can build a classifier to achieve a higher classification accuracy, which is demonstrated via cancer subtyping.
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca <zhipeng@cs.ualberta.ca>
authors.forename: Zhipeng
authors.initials: Z
authors.lastname: Cai
authors.forename: Randy
authors.initials: R
authors.lastname: Goebel
authors.forename: Mohammad R
authors.initials: MR
authors.lastname: Salavatipour
authors.forename: Guohui
authors.initials: G
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D005810:Multigene Family / Q000235:genetics; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins