title: Identifying a small set of marker genes using minimum expected cost of misclassification.
abstract: OBJECTIVE
  This paper presents a model independent feature selection approach to identify a small subset of marker genes.
  
  
  METHODS
  An evaluation measure, minimum expected cost of misclassification (MEMC), is used to estimate the discriminative power of a feature subset without building a model. The MECM measure is combined with sequential forward search for feature selection. This approach was applied to a breast cancer profiling problem, with the goal of identifying a small number of marker genes whose expression can be used to predict cancer molecular subtype (p53 gene status). Furthermore, the method was also applied to find a small set of single-nucleotide polymorphisms (SNPs) that can be used to predict molecular phenotype of a different type, namely alleles (genetic variants) of human leukocyte antigen genes that play an important roles in autoimmunity.
  
  
  RESULTS
  Two marker genes were identified based on p53 status, which achieved a p-value of 7.53×10(-5) (vs. 6×10(-4) with 32 genes identified by previous research) in survival analysis. Six SNP loci were identified that achieved a leave-one-out cross-validation accuracy of 92.8% (vs. 90.6% and 89.5% with 18 SNPs selected using χ2 statistics and information gain, respectively).
  
  
  CONCLUSIONS
  The MECM-based feature selection approach is capable of identifying a smaller subset of market genes with comparable or even better performance than that obtained using conventional filter methods.
authors.affiliation: School of Dynamic Systems, University of Cincinnati, 2600 Clifton Ave., Cincinnati, OH 45221, USA. sam.huang@uc.edu
authors.forename: Samuel H
authors.initials: SH
authors.lastname: Huang
authors.forename: Dengyao
authors.initials: D
authors.lastname: Mo
authors.forename: Jarek
authors.initials: J
authors.lastname: Meller
authors.forename: Michael
authors.initials: M
authors.lastname: Wagner
mesh_terms: D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000145:classification* / Q000235:genetics*; D003198:Computer Simulation; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D005819:Genetic Markers; D006680:HLA Antigens; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012984:Software
chemical_list: D005819:Genetic Markers; D006680:HLA Antigens