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- Title
A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.
- Authors
Mahmoud, Osama; Harrison, Andrew; Perperoglou, Aris; Gul, Asma; Khan, Zardad; Metodiev, Metodi V.; Lausen, Berthold
- 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.
- Publication
BMC Bioinformatics, 2014, Vol 15, Issue 1, p1
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/1471-2105-15-274