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...

Full description

Saved in:

Bibliographic Details
Published in:	BMC bioinformatics Vol. 11; no. 1; p. 447
Main Authors:	Guo, Yu, Graber, Armin, McBurney, Robert N, Balasubramanian, Raji
Format:	Journal Article
Language:	English
Published:	London BioMed Central 03.09.2010 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!