ArticleBioMed research international2015
mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Hybrid Gene Selection Algorithm for Cancer Classification Using Nuclear Reaction Optimization (NRO).Current issues in molecular biology · 2025Article
- GNNs and ensemble models enhance the prediction of new sRNA-mRNA interactions in unseen conditions.BMC bioinformatics · 2025Article
- Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR.Bioengineering (Basel, Switzerland) · 2025Article
- Transforming Cancer Classification: The Role of Advanced Gene Selection.Diagnostics (Basel, Switzerland) · 2024Article
- A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis.BMC medical informatics and decision making · 2024Article
- RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques.Journal of cancer research and clinical oncology · 2024Article
- A machine learning-based model analysis for serum markers of liver fibrosis in chronic hepatitis B patients.Scientific reports · 2024Article
- The impact of Bayesian optimization on feature selection.Scientific reports · 2024Article
- Novel machine-learning analysis of SARS-CoV-2 infection in a subclinical nonhuman primate model using radiomics and blood biomarkers.Scientific reports · 2023Article
- An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection.iScience · 2023Article
- Article
- Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease.European radiology · 2023Article
- A new hybrid algorithm for three-stage gene selection based on whale optimization.Scientific reports · 2023Article
- Review
- Mutational Slime Mould Algorithm for Gene Selection.Biomedicines · 2022Article
- Nature-inspired metaheuristics model for gene selection and classification of biomedical microarray data.Medical & biological engineering & computing · 2022Article
- Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes.Journal of healthcare engineering · 2022Article
- ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data.BMC bioinformatics · 2021Article
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3 authors.
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Abstract
An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.