Essential guidelines for computational method benchmarking

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 determin...

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Bibliographic Details
Published in:Genome Biology 2019-06, Vol.20 (1), p.125-125, Article 125
Main Authors: Weber, Lukas M, Saelens, Wouter, Cannoodt, Robrecht, Soneson, Charlotte, Hapfelmeier, Alexander, Gardner, Paul P, Boulesteix, Anne-Laure, Saeys, Yvan, Robinson, Mark D
Format: Article
Language:English
Subjects:
Benchmarking
Benchmarks
Bias
Collaboration
Computational Biology - standards
Computer applications
Datasets as Topic
Guidelines as Topic
Publishing
Research Design
Review
Software
Studies
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Summary: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.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-019-1738-8