Mahmoud, Osama and Harrison, Andrew and Perperoglou, Aris and Gul, Asma and Khan, Zardad and Metodiev, Metodi V and Lausen, Berthold (2014) 'A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.' BMC Bioinformatics, 15 (1). 274-. ISSN 1471-2105

Preview

Text 1471-2105-15-274.pdf - Published Version Available under License Creative Commons Attribution. Download (591kB) | Preview

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.

Item Type:	Article
Uncontrolled Keywords:	Feature selection; Gene ranking; Microarray classification; Proportional overlap score; Gene mask; Minimum subset of genes
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH426 Genetics
Divisions:	Faculty of Science and Health Faculty of Science and Health > Mathematical Sciences, Department of Faculty of Science and Health > Life Sciences, School of
SWORD Depositor:	Elements
Depositing User:	Elements
Date Deposited:	14 Aug 2014 07:54
Last Modified:	15 Jan 2022 00:25
URI:	http://repository.essex.ac.uk/id/eprint/9960

Actions (login required)

View Item