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Mahmoud, O. 
ORCID: https://orcid.org/0000-0003-0342-6704, Harrison, A., Perperoglou, A., Gul, A., Khan, Z., Metodiev, M.V. and Lausen, B.
(2014)
A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.
BMC Bioinformatics.
ISSN 1471-2105
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 |
|---|---|
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School Faculty of Science > School of Biological Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 21 Oct 2026 10:41 |
| Last Modified: | 21 Oct 2026 10:41 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/104830 |
| DOI: | 10.1186/1471-2105-15-274 |
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