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

Garrett M Dancik1, Karin S Dorman

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

Bioinformatics (Oxford, England)
|July 19, 2008
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Summary

Gaussian processes (GPs) offer flexible statistical modeling for complex computer codes. The mlegp R package facilitates GP analysis and sensitivity analysis for biological systems.

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Area of Science:

  • Computational Biology
  • Statistical Modeling

Background:

  • Biological systems are often high-dimensional, non-linear, and resource-intensive to analyze.
  • Gaussian processes (GPs) are flexible statistical models suitable for complex computer code outputs.
  • The mlegp R package provides a tool for applying GPs to biological system models.

Purpose of the Study:

  • To introduce the mlegp R package for fitting Gaussian processes to computer model outputs.
  • To enable sensitivity analysis for identifying key input variables in complex models.
  • To facilitate the analysis of challenging biological system models.

Main Methods:

  • Utilizing Gaussian processes (GPs) for predictive modeling.
  • Implementing the mlegp R package in the R statistical environment.
  • Performing sensitivity analysis to determine input variable importance.

Main Results:

  • The mlegp package effectively fits GPs to computer model outputs.
  • Sensitivity analysis identifies and characterizes the effects of important model inputs.
  • The approach is well-suited for analyzing complex biological systems.

Conclusions:

  • Gaussian processes provide a powerful framework for analyzing complex computer models, particularly in biology.
  • The mlegp R package offers a practical solution for applying GPs and conducting sensitivity analysis.
  • This facilitates a deeper understanding of high-dimensional, non-linear biological systems.