ArticleBMC bioinformatics2011
Microarray-based cancer prediction using single genes.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.Computational and mathematical methods in medicine · 2018Article
- Identification of altered pathways in breast cancer based on individualized pathway aberrance score.Oncology letters · 2017Article
- Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data.BioData mining · 2017Article
- Identification of hub genes and pathways associated with hepatocellular carcinoma based on network strategy.Experimental and therapeutic medicine · 2016Article
- Multiple differential expression networks identify key genes in rectal cancer.Cancer biomarkers : section A of Disease markers · 2016Article
- Gene set enrichment and topological analyses based on interaction networks in pediatric acute lymphoblastic leukemia.Oncology letters · 2015Article
- Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification.PloS one · 2015Article
- Identification of Marker Genes for Cancer Based on Microarrays Using a Computational Biology Approach.Current bioinformatics · 2014Article
- A comparative analysis of swarm intelligence techniques for feature selection in cancer classification.TheScientificWorldJournal · 2014Article
- A novel strategy for gene selection of microarray data based on gene-to-class sensitivity information.PloS one · 2014Article
- Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?Briefings in bioinformatics · 2013Article
- Improved shrunken centroid classifiers for high-dimensional class-imbalanced data.BMC bioinformatics · 2013Article
- Recognition of multiple imbalanced cancer types based on DNA microarray data using ensemble classifiers.BioMed research international · 2013Article
- MIDClass: microarray data classification by association rules and gene expression intervals.PloS one · 2013Article
- Gene signatures revisited.Journal of the National Cancer Institute · 2012Article
- Robust two-gene classifiers for cancer prediction.Genomics · 2012Article
- Algebraic comparison of partial lists in bioinformatics.PloS one · 2012Article
- Biomarker Discovery Based on Hybrid Optimization Algorithm and Artificial Neural Networks on Microarray Data for Cancer Classification.Journal of medical signals and sensorsArticle
- Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine.Journal of medical signals and sensorsArticle
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough numerous methods of using microarray data analysis for cancer classification have been proposed, most utilize many genes to achieve accurate classification. This can hamper interpretability of the models and ease of translation to other assay platforms. We explored the use of single genes to construct classification models. We first identified the genes with the most powerful univariate class discrimination ability and then constructed simple classification rules for class prediction using the single genes.
resultsWe applied our model development algorithm to eleven cancer gene expression datasets and compared classification accuracy to that for standard methods including Diagonal Linear Discriminant Analysis, k-Nearest Neighbor, Support Vector Machine and Random Forest. The single gene classifiers provided classification accuracy comparable to or better than those obtained by existing methods in most cases. We analyzed the factors that determined when simple single gene classification is effective and when more complex modeling is warranted.
conclusionsFor most of the datasets examined, the single-gene classification methods appear to work as well as more standard methods, suggesting that simple models could perform well in microarray-based cancer prediction.
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