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Bioinformatics Advance Access originally published online on January 28, 2009
Bioinformatics 2009 25(6):827-829; doi:10.1093/bioinformatics/btp062
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Parallelized prediction error estimation for evaluation of high-dimensional models

Christine Porzelius *, Harald Binder and Martin Schumacher

Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, 79104 Freiburg, Germany

*To whom correspondence should be addressed.


  Abstract

Summary: 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

Contact: cp{at}fdm.uni-freiburg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on July 23, 2008; revised on January 21, 2009; accepted on January 26, 2009

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