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Bioinformatics Advance Access originally published online on July 17, 2008
Bioinformatics 2008 24(17):1966-1967; 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, USA

*To whom correspondence should be addressed.


  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@iastate.edu

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

Associate Editor: John Quackenbush

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

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