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

• 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: England BioMed Central Ltd 03.09.2010; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Animal models; Animals; Biological markers; Biomarkers; Classification - methods; Comparative studies; Computational biology; Databases, Factual; Design; Discriminant analysis; Gene expression; Gene Expression Profiling - methods; Genetic algorithms; Humans; Mass spectrometry; Methods; Models, Statistical; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated; Sample Size; Standard deviation; Studies; United States; United Kingdom

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 goal is the...