Improved high-dimensional prediction with Random Forests by the use of co-data

Improved high-dimensional prediction with Random Forests by the use of co-data

Abstract Background Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary ‘co-data’ can be used to improve the performance of a Random Forest in such a setting. Results Co-data are incorporated in the Rando...

Full description

Bibliographic Details
Main Authors: Dennis E. te Beest, Steven W. Mes, Saskia M. Wilting, Ruud H. Brakenhoff, Mark A. van de Wiel
Format: Article
Language:English
Published: BMC 2017-12-01
Series:BMC Bioinformatics
Subjects:
Classification
Random forest
Gene expression
Methylation
DNA copy number
Prior information
Online Access:http://link.springer.com/article/10.1186/s12859-017-1993-1

Internet

http://link.springer.com/article/10.1186/s12859-017-1993-1

Similar Items