ArticlePeerJ. Computer science2021
Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 14 citations in OpenAlex.
- A Robust Masked Painter Framework for Gene Selection in Binary Classification of High-Dimensional Functional Genomic Data.Entropy (Basel, Switzerland) · 2026Article
- Article
- Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio.PloS one · 2023Article
- An Efficient AP-ANN-Based Multimethod Fusion Model to Detect Stress through EEG Signal Analysis.Computational intelligence and neuroscience · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD). This method robustly identifies the most discriminative genes by considering the overlapping scores of the gene expression values for binary class problems. Genes with a high degree of overlap between classes are discarded and the ones that discriminate between the classes are selected. The results of the proposed method are compared with five state-of-the-art gene selection methods based on classification error, Brier score, and sensitivity, by considering eleven gene expression datasets. Classification of observations for different sets of selected genes by the proposed method is carried out by three different classifiers, i.e., random forest, k-nearest neighbors (k-NN), and support vector machine (SVM). Box-plots and stability scores of the results are also shown in this paper. The results reveal that in most of the cases the proposed method outperforms the other methods.
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