title: Structured polychotomous machine diagnosis of multiple cancer types using gene expression.
abstract: BACKGROUND
  The problem of class prediction has received a tremendous amount of attention in the literature recently. In the context of DNA microarrays, where the task is to classify and predict the diagnostic category of a sample on the basis of its gene expression profile, a problem of particular importance is the diagnosis of cancer type based on microarray data. One method of classification which has been very successful in cancer diagnosis is the support vector machine (SVM). The latter has been shown (through simulations) to be superior in comparison with other methods, such as classical discriminant analysis, however, SVM suffers from the drawback that the solution is implicit and therefore is difficult to interpret. In order to remedy this difficulty, an analysis of variance decomposition using structured kernels is proposed and is referred to as the structured polychotomous machine. This technique utilizes Newton-Raphson to find estimates of coefficients followed by the Rao and Wald tests, respectively, for addition and deletion of import vectors.
  
  
  RESULTS
  The proposed method is applied to microarray data and simulation data. The major breakthrough of our method is efficiency in that only a minimal number of genes that accurately predict the classes are selected. It has been verified that the selected genes serve as legitimate markers for cancer classification from a biological point of view.
  
  
  BACKGROUND
  All source codes used are available on request from the authors.
authors.affiliation: Department of Statistics, Korea University, Seoul 136-701, Korea. jykoo@korea.ac.kr
authors.forename: Ja-Yong
authors.initials: JY
authors.lastname: Koo
authors.forename: Insuk
authors.initials: I
authors.lastname: Sohn
authors.forename: Sujong
authors.initials: S
authors.lastname: Kim
authors.forename: Jae Won
authors.initials: JW
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis* / Q000235:genetics / Q000378:metabolism; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis* / Q000235:genetics / Q000378:metabolism; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins