ArticleBMC bioinformatics2019
Deep gene selection method to select genes from microarray datasets for cancer classification.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin.International journal of clinical oncology · 2024Article
- A gene selection algorithm for microarray cancer classification using an improved particle swarm optimization.Scientific reports · 2024Article
- Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.BMC bioinformatics · 2024Article
- Radiation Type- and Dose-Specific Transcriptional Responses across Healthy and Diseased Mammalian Tissues.Antioxidants (Basel, Switzerland) · 2022Article
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4 authors.
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Abstract
backgroundMicroarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis.
resultsThe gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost.
conclusionsWe provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples' classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.
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