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Related Experiment Video

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Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
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Over-optimism in bioinformatics: an illustration.

Monika Jelizarow1, Vincent Guillemot, Arthur Tenenhaus

  • 1Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany.

Bioinformatics (Oxford, England)
|June 29, 2010
PubMed
Summary
This summary is machine-generated.

Statistical bioinformatics research often suffers from over-optimism due to various optimization methods. This study demonstrates how optimizing datasets, settings, and methods can artificially inflate results, highlighting the need for independent validation data.

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Area of Science:

  • Statistical Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Optimization mechanisms in statistical bioinformatics research can lead to 'over-optimism' in published findings.
  • A systematic study on the sources of this over-optimism is currently lacking.

Purpose of the Study:

  • To empirically investigate the sources of over-optimism in statistical bioinformatics.
  • To demonstrate how 'fishing for significance' can artificially inflate the perceived performance of new algorithms.

Main Methods:

  • Empirical study using high-dimensional classification as an example.
  • Evaluation of a novel linear discriminant analysis incorporating prior knowledge on gene functional groups.
  • Quantitative analysis of over-optimism sources: dataset, settings, competing methods, and method characteristics.

Main Results:

  • The novel classification algorithm, despite poor error rates, can appear superior to existing methods through 'fishing for significance'.
  • Sources of over-optimism were quantitatively identified, including dataset, settings, competing methods, and method characteristics optimization.
  • The study demonstrates the potential for artificial superiority if results are not validated on independent data.

Conclusions:

  • Improvement of quantitative criteria like error rate should be validated on independent data.
  • The study underscores the importance of rigorous validation to prevent over-optimism in bioinformatics research.
  • Ensuring reproducibility through publicly available code and data is crucial for scientific integrity.