ArticleGenomics, proteomics & bioinformatics2009
A modified ant colony optimization algorithm for tumor marker gene selection.
Article in Genomics, proteomics & bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The Application of Microsatellite Markers as Molecular Tools for Studying Genomic Variability in Vertebrate Populations.Current issues in molecular biology · 2025Review
- 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
- Article
- A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia.BioMed research international · 2022Article
- A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm.BMC medical informatics and decision making · 2021Article
- Dimensionality reduction using singular vectors.Scientific reports · 2021Article
- A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification.Scientific reports · 2019Article
- Gene Selection via a New Hybrid Ant Colony Optimization Algorithm for Cancer Classification in High-Dimensional Data.Computational and mathematical methods in medicine · 2019Article
- DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles.Saudi journal of biological sciences · 2018Article
- Co-ABC: Correlation artificial bee colony algorithm for biomarker gene discovery using gene expression profile.Saudi journal of biological sciences · 2018Article
- Gene selection for cancer classification with the help of bees.BMC medical genomics · 2016Article
- mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.BioMed research international · 2015Article
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5 authors.
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Abstract
Microarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes but only a few hundreds of samples or less. Such extreme asymmetry between the dimensionality of genes and samples can lead to inaccurate diagnosis of disease in clinic. Therefore, it has been shown that selecting a small set of marker genes can lead to improved classification accuracy. In this paper, a simple modified ant colony optimization (ACO) algorithm is proposed to select tumor-related marker genes, and support vector machine (SVM) is used as classifier to evaluate the performance of the extracted gene subset. Experimental results on several benchmark tumor microarray datasets showed that the proposed approach produces better recognition with fewer marker genes than many other methods. It has been demonstrated that the modified ACO is a useful tool for selecting marker genes and mining high dimension data.
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