title: Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data.
abstract: BACKGROUND
  Using DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant).
  
  
  RESULTS
  The proposed models were tested on four published datasets: (1) Leukemia (2) Colon cancer (3) Brain tumors and (4) NCI cancer cell lines. The models gave class prediction with markedly reduced error rates compared to other class prediction approaches, and the importance of feature selection on microarray data analysis was also emphasized.
  
  
  CONCLUSIONS
  Our models identify marker genes with predictive potential, often better than other available methods in the literature. The models are potentially useful for medical diagnostics and may reveal some insights into cancer classification. Additionally, we illustrated two limitations in tumor classification from microarray data related to the biology underlying the data, in terms of (1) the class size of data, and (2) the internal structure of classes. These limitations are not specific for the classification models used.
authors.affiliation: Department of Tumor Biology, The Norwegian Radium Hospital, N0310 Oslo, Norway. junbaiw@radium.uio.no
authors.forename: Junbai
authors.initials: J
authors.lastname: Wang
authors.forename: Trond Hellem
authors.initials: TH
authors.lastname: Bø
authors.forename: Inge
authors.initials: I
authors.lastname: Jonassen
authors.forename: Ola
authors.initials: O
authors.lastname: Myklebost
authors.forename: Eivind
authors.initials: E
authors.lastname: Hovig
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D001932:Brain Neoplasms / Q000145:classification / Q000235:genetics; D045744:Cell Line, Tumor; D002874:Chromosome Mapping / Q000379:methods / Q000706:statistics & numerical data; D016000:Cluster Analysis; D003110:Colonic Neoplasms / Q000145:classification / Q000235:genetics; D019295:Computational Biology / Q000706:statistics & numerical data; D005260:Female; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D052138:Genes, Neoplasm / Q000235:genetics*; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000235:genetics; D009369:Neoplasms / Q000145:classification* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010051:Ovarian Neoplasms / Q000145:classification / Q000235:genetics / Q000473:pathology; D011237:Predictive Value of Tests
chemical_list: D014408:Biomarkers, Tumor