ArticleBMC bioinformatics2014
A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Robust Masked Painter Framework for Gene Selection in Binary Classification of High-Dimensional Functional Genomic Data.Entropy (Basel, Switzerland) · 2026Article
- Double weighted k nearest neighbours for binary classification of high dimensional genomic data.Scientific reports · 2025Article
- Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Feature selection via robust weighted score for high dimensional binary class-imbalanced gene expression data.Heliyon · 2024Article
- An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks.Scientific reports · 2024Article
- Tumor-suppressive function of EZH2 is through inhibiting glutaminase.Cell death & disease · 2021Article
- Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan.Journal of healthcare engineering · 2021Article
- Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments.PeerJ. Computer science · 2021Article
- Ensemble of a subset ofAdvances in data analysis and classification · 2018Article
- Data on clinical significance of GAS2 in colorectal cancer cells.Data in brief · 2016Article
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Authors and funding
7 authors.
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Abstract
backgroundMicroarray 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.
resultsWe 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.
conclusionsA 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.
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