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Essential guidelines for computational method benchmarking

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Abstract
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.
Keywords
DIFFERENTIAL EXPRESSION, REPRODUCIBLE RESEARCH, METAANALYSIS METHODS, DATA SETS, RNA, BIOINFORMATICS, METAGENOME, SOFTWARE, SEQUENCE, DESIGN

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Citation

Please use this url to cite or link to this publication:

MLA
Weber, Lukas M. et al. “Essential Guidelines for Computational Method Benchmarking.” GENOME BIOLOGY 20 (2019): n. pag. Print.
APA
Weber, L. M., Saelens, W., Cannoodt, R., Soneson, C., Hapfelmeier, A., Gardner, P. P., Boulesteix, A.-L., et al. (2019). Essential guidelines for computational method benchmarking. GENOME BIOLOGY, 20.
Chicago author-date
Weber, Lukas M., Wouter Saelens, Robrecht Cannoodt, Charlotte Soneson, Alexander Hapfelmeier, Paul P. Gardner, Anne-Laure Boulesteix, Yvan Saeys, and Mark D. Robinson. 2019. “Essential Guidelines for Computational Method Benchmarking.” Genome Biology 20.
Chicago author-date (all authors)
Weber, Lukas M., Wouter Saelens, Robrecht Cannoodt, Charlotte Soneson, Alexander Hapfelmeier, Paul P. Gardner, Anne-Laure Boulesteix, Yvan Saeys, and Mark D. Robinson. 2019. “Essential Guidelines for Computational Method Benchmarking.” Genome Biology 20.
Vancouver
1.
Weber LM, Saelens W, Cannoodt R, Soneson C, Hapfelmeier A, Gardner PP, et al. Essential guidelines for computational method benchmarking. GENOME BIOLOGY. London: Bmc; 2019;20.
IEEE
[1]
L. M. Weber et al., “Essential guidelines for computational method benchmarking,” GENOME BIOLOGY, vol. 20, 2019.
@article{8622896,
  abstract     = {In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.},
  articleno    = {125},
  author       = {Weber, Lukas M. and Saelens, Wouter and Cannoodt, Robrecht and Soneson, Charlotte and Hapfelmeier, Alexander and Gardner, Paul P. and Boulesteix, Anne-Laure and Saeys, Yvan and Robinson, Mark D.},
  issn         = {1474-760X},
  journal      = {GENOME BIOLOGY},
  keywords     = {DIFFERENTIAL EXPRESSION,REPRODUCIBLE RESEARCH,METAANALYSIS METHODS,DATA SETS,RNA,BIOINFORMATICS,METAGENOME,SOFTWARE,SEQUENCE,DESIGN},
  language     = {eng},
  pages        = {12},
  publisher    = {Bmc},
  title        = {Essential guidelines for computational method benchmarking},
  url          = {http://dx.doi.org/10.1186/s13059-019-1738-8},
  volume       = {20},
  year         = {2019},
}

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