title: Class discovery and classification of tumor samples using mixture modeling of gene expression data--a unified approach.
abstract: BACKGROUND
  The DNA microarray technology has been increasingly used in cancer research. In the literature, discovery of putative classes and classification to known classes based on gene expression data have been largely treated as separate problems. This paper offers a unified approach to class discovery and classification, which we believe is more appropriate, and has greater applicability, in practical situations.
  
  
  RESULTS
  We model the gene expression profile of a tumor sample as from a finite mixture distribution, with each component characterizing the gene expression levels in a class. The proposed method was applied to a leukemia dataset, and good results are obtained. With appropriate choices of genes and preprocessing method, the number of leukemia types and subtypes is correctly inferred, and all the tumor samples are correctly classified into their respective type/subtype. Further evaluation of the method was carried out on other variants of the leukemia data and a colon dataset.
authors.affiliation: Department of Statistics, Ohio State University, 1958 Neil Avenue, Columbus, OH 43210, USA.
authors.forename: Roxana
authors.initials: R
authors.lastname: Alexandridis
authors.forename: Shili
authors.initials: S
authors.lastname: Lin
authors.forename: Mark
authors.initials: M
authors.lastname: Irwin
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D005820:Genetic Testing / Q000379:methods; D006801:Humans; D007938:Leukemia / Q000145:classification* / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000145:classification / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D009363:Neoplasm Proteins