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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2019
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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 |
author_sort | Kreutz, Clemens |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-6915982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kreutzclemens guidelinesforbenchmarkingofoptimizationbasedapproachesforfittingmathematicalmodels |