Literature DB >> 18635570

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

Garrett M Dancik1, Karin S Dorman.   

Abstract

UNLABELLED: 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

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Year:  2008        PMID: 18635570      PMCID: PMC2732217          DOI: 10.1093/bioinformatics/btn329

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  3 in total

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Authors:  Hiroaki Kitano
Journal:  Nature       Date:  2002-11-14       Impact factor: 49.962

2.  Computational methods for diffusion-influenced biochemical reactions.

Authors:  Maciej Dobrzynski; Jordi Vidal Rodríguez; Jaap A Kaandorp; Joke G Blom
Journal:  Bioinformatics       Date:  2007-05-30       Impact factor: 6.937

3.  Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model.

Authors:  Jose L Segovia-Juarez; Suman Ganguli; Denise Kirschner
Journal:  J Theor Biol       Date:  2004-12-07       Impact factor: 2.691

  3 in total
  1 in total

1.  High-resolution computational modeling of immune responses in the gut.

Authors:  Meghna Verma; Josep Bassaganya-Riera; Andrew Leber; Nuria Tubau-Juni; Stefan Hoops; Vida Abedi; Xi Chen; Raquel Hontecillas
Journal:  Gigascience       Date:  2019-06-01       Impact factor: 6.524

  1 in total

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