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A plea for neutral comparison studies in computational sciences.

Anne-Laure Boulesteix1, Sabine Lauer, Manuel J A Eugster

  • 1Department of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-University of Munich, Munich, Germany. boulesteix@ibe.med.uni-muenchen.de

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Summary
This summary is machine-generated.

Neutral comparison studies are crucial for evaluating computational methods objectively. This paper surveys current practices and defines criteria for rigorous, unbiased method comparisons in computational science.

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Area of Science:

  • Computational science, including bioinformatics, computational statistics, and machine learning.

Background:

  • Most computational science publications focus on new methods, with comparison studies receiving limited journal consideration despite reader appreciation.
  • Objective evaluation of existing methods and standard establishment are hindered by the lack of neutral comparison studies.

Purpose of the Study:

  • To survey current practices in method comparison within computational science literature.
  • To critically discuss the necessity, impact, and limitations of neutral comparison studies.
  • To define criteria for neutral comparison studies and outline components of a 'tidy neutral comparison study'.

Main Methods:

  • Survey of recent computational papers on supervised classification in seven high-ranking computational science journals.
  • Analysis of method comparison practices in articles presenting new methods and dedicated comparison studies.

Main Results:

  • Identified a gap between the appreciation of comparison studies and their publication frequency.
  • Established three criteria for a comparison study to be considered neutral.
  • Provided considerations for designing rigorous, neutral comparison studies.

Conclusions:

  • Neutral comparison studies are vital for objective method evaluation and standardization in computational science.
  • Clear guidelines and criteria are needed to promote high-quality, neutral comparison studies.
  • Encouraging such studies will advance the field by providing reliable benchmarks for computational methods.