Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms

Background Data generated using 'omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of subjects in the study. In this paper, we consider issues relevant in the design of biomedical studies in which the g...

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Vydáno v:	BMC bioinformatics Ročník 11; číslo 1; s. 447
Hlavní autoři:	Guo, Yu, Graber, Armin, McBurney, Robert N, Balasubramanian, Raji
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 03.09.2010 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Accuracy Algorithms Animal models Animals Bioinformatics Biological markers Biomarkers Biomedical and Life Sciences Classification - methods Comparative studies Computational biology Computational Biology/Bioinformatics Computer Appl. in Life Sciences Databases, Factual Design Discriminant analysis Gene expression Gene Expression Profiling - methods Genetic algorithms Humans Life Sciences Mass spectrometry Methods Microarrays Models, Statistical Oligonucleotide Array Sequence Analysis - methods Pattern Recognition, Automated Proteomics Research Article Sample Size Standard deviation Studies United States United Kingdom Simulated Dataset Recursive Feature Elimination Support Vector Machine Average Classification Accuracy Random Forest
ISSN:	1471-2105, 1471-2105
On-line přístup:	Získat plný text
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