Parallelized prediction error estimation for evaluation of high-dimensional models

Abstract

UNLABELLED There is a multitude of new techniques that promise to extract predictive information in bioinformatics applications. It has been recognized that a first step for validation of the resulting model fits should rely on proper use of resampling techniques. However, this advice is frequently not followed, potential reasons being difficulty of correct implementation and computational demand. This is addressed by the R package peperr, which is designed for reliable prediction error estimation through resampling, potentially accelerated by parallel execution on a compute cluster. Its interface allows easy connection to newly developed model fitting routines. Performance evaluation of the latter is furthermore guided by diagnostic plots, which helps to detect specific problems due to high-dimensional data structures. AVAILABILITY http://cran.r-project.org, http://www.imbi.uni-freiburg.de/parallel. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

DOI: 10.1093/bioinformatics/btp062

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Cite this paper

@article{Porzelius2009ParallelizedPE, title={Parallelized prediction error estimation for evaluation of high-dimensional models}, author={Christine Porzelius and Harald Binder and Martin Schumacher}, journal={Bioinformatics}, year={2009}, volume={25 6}, pages={827-9} }