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Effect of training-sample size and classification difficulty on the accuracy of genomic predictors
229
Zitationen
18
Autoren
2010
Jahr
Abstract
We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.
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Autoren
Institutionen
- SIB Swiss Institute of Bioinformatics(CH)
- Center for Devices and Radiological Health(US)
- United States Food and Drug Administration(US)
- Nuvera Biosciences (United States)(US)
- Vavilov Institute of General Genetics(RU)
- Cancer Research And Biostatistics(US)
- École Polytechnique Fédérale de Lausanne(CH)
- National Center for Toxicological Research(US)
- The University of Texas MD Anderson Cancer Center(US)