CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data
Open Access
- 16 October 2008
- journal article
- research article
- Published by Springer Nature in BMC Bioinformatics
- Vol. 9 (1), 439
- https://doi.org/10.1186/1471-2105-9-439
Abstract
For the last eight years, microarray-based classification has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the so-called "p ≫ n" setting where the number of predictors p by far exceeds the number of observations n, hence the term "ill-posed-problem". Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for statisticians without experience in this area or for scientists with limited statistical background. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers.Keywords
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