title: Optimization based tumor classification from microarray gene expression data.
abstract: BACKGROUND
  An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types.
  
  
  RESULTS
  We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described.
  
  
  CONCLUSIONS
  The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.
authors.affiliation: Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey.
authors.forename: Onur
authors.initials: O
authors.lastname: Dagliyan
authors.forename: Fadime
authors.initials: F
authors.lastname: Uney-Yuksektepe
authors.forename: I Halil
authors.initials: IH
authors.lastname: Kavakli
authors.forename: Metin
authors.initials: M
authors.lastname: Turkay
mesh_terms: D000465:Algorithms; D002138:Calibration; D001330:Electronic Data Processing / Q000592:standards; D020869:Gene Expression Profiling / Q000379:methods / Q000592:standards*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D016403:Lymphoma, Large B-Cell, Diffuse / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D008297:Male; D046228:Microarray Analysis / Q000379:methods / Q000592:standards*; D008962:Models, Theoretical; D009369:Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods / Q000592:standards; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000145:classification / Q000175:diagnosis / Q000235:genetics