ArticlePloS one2014
A novel strategy for gene selection of microarray data based on gene-to-class sensitivity information.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization.PeerJ. Computer science · 2025Article
- A gene selection algorithm for microarray cancer classification using an improved particle swarm optimization.Scientific reports · 2024Article
- An efficient gene selection method for microarray data based on LASSO and BPSO.BMC bioinformatics · 2019Article
- Deep gene selection method to select genes from microarray datasets for cancer classification.BMC bioinformatics · 2019Article
- A hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization.BMC bioinformatics · 2019Article
- An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.PloS one · 2016Article
- A regression-based differential expression detection algorithm for microarray studies with ultra-low sample size.PloS one · 2015Article
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3 authors.
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Abstract
To obtain predictive genes with lower redundancy and better interpretability, a hybrid gene selection method encoding prior information is proposed in this paper. To begin with, the prior information referred to as gene-to-class sensitivity (GCS) of all genes from microarray data is exploited by a single hidden layered feedforward neural network (SLFN). Then, to select more representative and lower redundant genes, all genes are grouped into some clusters by K-means method, and some low sensitive genes are filtered out according to their GCS values. Finally, a modified binary particle swarm optimization (BPSO) encoding the GCS information is proposed to perform further gene selection from the remainder genes. For considering the GCS information, the proposed method selects those genes highly correlated to sample classes. Thus, the low redundant gene subsets obtained by the proposed method also contribute to improve classification accuracy on microarray data. The experiments results on some open microarray data verify the effectiveness and efficiency of the proposed approach.
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