ArticleBMC bioinformatics2010
Simple and flexible classification of gene expression microarrays via Swirls and Ripples.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Evaluating Markers for Guiding Treatment.Journal of the National Cancer Institute · 2016Article
- Evaluating surrogate endpoints, prognostic markers, and predictive markers: Some simple themes.Clinical trials (London, England) · 2015Article
- A cancer theory kerfuffle can lead to new lines of research.Journal of the National Cancer Institute · 2015Article
- Systems analysis of high-throughput data.Advances in experimental medicine and biology · 2014Review
- Bivariate marker measurements and ROC analysis.Biometrics · 2012Article
- Gene signatures revisited.Journal of the National Cancer Institute · 2012Article
- Robust two-gene classifiers for cancer prediction.Genomics · 2012Article
- Partition decoupling for multi-gene analysis of gene expression profiling data.BMC bioinformatics · 2011Article
- Microarray-based cancer prediction using single genes.BMC bioinformatics · 2011Article
- Systems biology and cancer: promises and perils.Progress in biophysics and molecular biology · 2011Review
- Designing a randomized clinical trial to evaluate personalized medicine: a new approach based on risk prediction.Journal of the National Cancer Institute · 2010Article
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Abstract
backgroundA simple classification rule with few genes and parameters is desirable when applying a classification rule to new data. One popular simple classification rule, diagonal discriminant analysis, yields linear or curved classification boundaries, called Ripples, that are optimal when gene expression levels are normally distributed with the appropriate variance, but may yield poor classification in other situations.
resultsA simple modification of diagonal discriminant analysis yields smooth highly nonlinear classification boundaries, called Swirls, that sometimes outperforms Ripples. In particular, if the data are normally distributed with different variances in each class, Swirls substantially outperforms Ripples when using a pooled variance to reduce the number of parameters. The proposed classification rule for two classes selects either Swirls or Ripples after parsimoniously selecting the number of genes and distance measures. Applications to five cancer microarray data sets identified predictive genes related to the tissue organization theory of carcinogenesis.
conclusionThe parsimonious selection of classifiers coupled with the selection of either Swirls or Ripples provides a good basis for formulating a simple, yet flexible, classification rule. Open source software is available for download.
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