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Genome Biol. 2019 Jun 20;20(1):125. doi: 10.1186/s13059-019-1738-8.

Essential guidelines for computational method benchmarking.

Author information

1
Institute of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland.
2
SIB Swiss Institute of Bioinformatics, University of Zurich, 8057, Zurich, Switzerland.
3
Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, 9052, Ghent, Belgium.
4
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000, Ghent, Belgium.
5
Present address: Friedrich Miescher Institute for Biomedical Research and SIB Swiss Institute of Bioinformatics, 4058, Basel, Switzerland.
6
Institute of Medical Informatics, Statistics and Epidemiology, Technical University of Munich, 81675, Munich, Germany.
7
Department of Biochemistry, University of Otago, Dunedin, 9016, New Zealand.
8
Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-University, 81377, Munich, Germany.
9
Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, 9052, Ghent, Belgium. yvan.saeys@ugent.be.
10
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000, Ghent, Belgium. yvan.saeys@ugent.be.
11
Institute of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland. mark.robinson@imls.uzh.ch.
12
SIB Swiss Institute of Bioinformatics, University of Zurich, 8057, Zurich, Switzerland. mark.robinson@imls.uzh.ch.

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.

PMID:
31221194
PMCID:
PMC6584985
DOI:
10.1186/s13059-019-1738-8
[Indexed for MEDLINE]
Free PMC Article

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