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A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

Mahmoud, O and Harrison, A and Perperoglou, A and Gul, A and Khan, Z and Metodiev, MV and Lausen, B (2014) 'A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.' BMC Bioinformatics, 15 (1). ISSN 1471-2105

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Abstract

© 2014 Mahmoud et al.; licensee BioMed Central Ltd. 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
Subjects: Q Science > QA Mathematics
Q Science > QH Natural history > QH426 Genetics
Divisions: Faculty of Science and Health > Biological Sciences, School of
Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 14 Aug 2014 07:54
Last Modified: 23 Jan 2019 00:18
URI: http://repository.essex.ac.uk/id/eprint/9960

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