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Bioinformatics Advance Access published online on July 17, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn329
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

mlegp: statistical analysis for computer models of biological systems using R

Garrett M. Dancik 1,2 and Karin S. Dorman 1,2,3,*

1Program in Bioinformatics & Computational Biology, 2Department of Statistics, and 3Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50010.

*To whom correspondence should be addressed. Karin S. Dorman, E-mail: kdorman{at}iastate.edu


  Abstract

Summary: Gaussian processes (GPs) are flexible statistical models commonly used for predicting output from complex computer codes. As such, GPs are well-suited for the analysis of computer models of biological systems, which have been traditionally difficult to analyze due to their high-dimensional, non-linear, and resource-intensive nature. We describe an R package, mlegp, that fits GPs to computer model outputs and performs sensitivity analysis to identify and characterize the effects of important model inputs.

Availability: http://www.biomath.org/mlegp

Contact: kdorman{at}iastate.edu

Supplementary information: See http://www.biomath.org/mlegp for a user manual and examples.

Associate Editor: Prof. John Quackenbush

Received on October 7, 2007; revised on May 2, 2008; accepted on June 23, 2008

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