title: A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.
abstract: BACKGROUND
  Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task.
  
  
  RESULTS
  We apply POS, along-with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.
  
  
  CONCLUSIONS
  A novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along-with a novel gene score are exploited to produce the selected subset of genes.
authors.affiliation: Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, UK. ofamah@essex.ac.uk.
authors.forename: Osama
authors.initials: O
authors.lastname: Mahmoud
authors.forename: Andrew
authors.initials: A
authors.lastname: Harrison
authors.forename: Aris
authors.initials: A
authors.lastname: Perperoglou
authors.forename: Asma
authors.initials: A
authors.lastname: Gul
authors.forename: Zardad
authors.initials: Z
authors.lastname: Khan
authors.forename: Metodi V
authors.initials: MV
authors.lastname: Metodiev
authors.forename: Berthold
authors.initials: B
authors.lastname: Lausen
mesh_terms: D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D060388:Support Vector Machine
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