title: A semiparametric approach for marker gene selection based on gene expression data.
abstract: BACKGROUND
  Identification of differentially expressed genes is a major issue in gene expression data analysis and selection of marker genes is critical in tumor classification using gene expression data. In this paper, we propose a semiparametric two-sample test to identify both differentially expressed genes and select marker genes for sample classification.
  
  
  RESULTS
  A simulation study shows that the proposed method is more robust and powerful than the methods, generally used such as t-tests and non-parametric rank-sum tests, when the sample size is small. Cross-validation shows that the sample classification based on genes selected using this semiparametric method has lower misclassification rates.
  
  
  BACKGROUND
  hongyu.zhao@yale.edu.
authors.affiliation: Department of Mathematical Sciences, Indiana University South Bend South Bend, IN 46634, USA.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Guan
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005820:Genetic Testing / Q000379:methods*; D006801:Humans; D007938:Leukemia / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism*; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D005819:Genetic Markers; D009363:Neoplasm Proteins