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Guidelines for benchmarking of optimization-based approaches for fitting mathematical models

Insufficient performance of optimization-based approaches for the fitting of mathematical models is still a major bottleneck in systems biology. In this article, the reasons and methodological challenges are summarized as well as their impact in benchmark studies. Important aspects for achieving an...

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Autor principal: Kreutz, Clemens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915982/
https://www.ncbi.nlm.nih.gov/pubmed/31842943
http://dx.doi.org/10.1186/s13059-019-1887-9
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author Kreutz, Clemens
author_facet Kreutz, Clemens
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description Insufficient performance of optimization-based approaches for the fitting of mathematical models is still a major bottleneck in systems biology. In this article, the reasons and methodological challenges are summarized as well as their impact in benchmark studies. Important aspects for achieving an increased level of evidence for benchmark results are discussed. Based on general guidelines for benchmarking in computational biology, a collection of tailored guidelines is presented for performing informative and unbiased benchmarking of optimization-based fitting approaches. Comprehensive benchmark studies based on these recommendations are urgently required for the establishment of a robust and reliable methodology for the systems biology community.
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spelling pubmed-69159822019-12-30 Guidelines for benchmarking of optimization-based approaches for fitting mathematical models Kreutz, Clemens Genome Biol Review Insufficient performance of optimization-based approaches for the fitting of mathematical models is still a major bottleneck in systems biology. In this article, the reasons and methodological challenges are summarized as well as their impact in benchmark studies. Important aspects for achieving an increased level of evidence for benchmark results are discussed. Based on general guidelines for benchmarking in computational biology, a collection of tailored guidelines is presented for performing informative and unbiased benchmarking of optimization-based fitting approaches. Comprehensive benchmark studies based on these recommendations are urgently required for the establishment of a robust and reliable methodology for the systems biology community. BioMed Central 2019-12-16 /pmc/articles/PMC6915982/ /pubmed/31842943 http://dx.doi.org/10.1186/s13059-019-1887-9 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Review
Kreutz, Clemens
Guidelines for benchmarking of optimization-based approaches for fitting mathematical models
title Guidelines for benchmarking of optimization-based approaches for fitting mathematical models
title_full Guidelines for benchmarking of optimization-based approaches for fitting mathematical models
title_fullStr Guidelines for benchmarking of optimization-based approaches for fitting mathematical models
title_full_unstemmed Guidelines for benchmarking of optimization-based approaches for fitting mathematical models
title_short Guidelines for benchmarking of optimization-based approaches for fitting mathematical models
title_sort guidelines for benchmarking of optimization-based approaches for fitting mathematical models
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915982/
https://www.ncbi.nlm.nih.gov/pubmed/31842943
http://dx.doi.org/10.1186/s13059-019-1887-9
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