Essential guidelines for computational method benchmarking

Lukas M. Weber

doi:10.1186/s13059-019-1738-8

2019-06-20

Alexander Hapfelmeier

Paul P. Gardner

Anne‐Laure Boulesteix

Yvan Saeys *

Mark D. Robinson *

DOI：10.1186/s13059-019-1738-8

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摘要

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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.

Keyword:

DIFFERENTIAL EXPRESSION

REPRODUCIBLE RESEARCH

METAANALYSIS METHODS

DATA SETS

RNA

BIOINFORMATICS

METAGENOME

SOFTWARE

SEQUENCE

DESIGN

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Genome Biology

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