Kit C. B. Roes
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Kit C. B. Roes has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Marinus Jc Eijkemans
Julius Center for Health Sciences and Primary care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
1 shared publication - Putri W. Novianti
Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands.
1 shared publication - Victor L. Jong
Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands.
1 shared publication
Latest Publications
- Open Access
- 21 June 2015
- Published by Springer Nature in BMC Bioinformatics
- Vol. 16 (1) , 199
Factors affecting the accuracy of a class prediction model in gene expression data
Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer.
- 21 June 2015
- Published by Springer Nature in BMC Bioinformatics
- Vol. 16 (1) , 199
Open AccessFactors affecting the accuracy of a class prediction model in gene expression data
Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer.