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Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation

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  • Hand David J

    (Imperial College, London)

Abstract

The performance results of a wide range of different classifiers applied to proteomic mass spectra data, in a blind comparative assessment organised by Bart Mertens, are reviewed. The different approaches are summarised, issues of how to evaluate and compare the predictions are described, and the results of the different methods are examined. Although the different methods perform differently, their rank ordering varies according to how one measures performance, so that one cannot draw unequivocal conclusions about which is 'best.' Instead, it is clear that what matters is not the method by itself, but the interaction of method and user - the degree of sophistication of the user with a method. Nevertheless, such competitions do serve the useful role of setting (constantly improving) baselines against which new researchers can pit their wits and methods, as well as providing standards against which new methods should be assessed.

Suggested Citation

  • Hand David J, 2008. "Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-23, December.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:2:n:15
    DOI: 10.2202/1544-6115.1435
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    References listed on IDEAS

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    1. Gammerman Alex & Nouretdinov Ilia & Burford Brian & Chervonenkis Alexey & Vovk Vladimir & Luo Zhiyuan, 2008. "Clinical Mass Spectrometry Proteomic Diagnosis by Conformal Predictors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-12, July.
    2. Barrett Jennifer H & Cairns David A, 2008. "Application of the Random Forest Classification Method to Peaks Detected from Mass Spectrometric Proteomic Profiles of Cancer Patients and Controls," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-22, February.
    3. Fearn Tom, 2008. "Principal Component Discriminant Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-6, February.
    4. Datta Somnath, 2008. "Classification of Breast Cancer versus Normal Samples from Mass Spectrometry Profiles Using Linear Discriminant Analysis of Important Features Selected by Random Forest," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-14, February.
    5. Valkenborg Dirk & Van Sanden Suzy & Lin Dan & Kasim Adetayo & Zhu Qi & Haldermans Philippe & Jansen Ivy & Shkedy Ziv & Burzykowski Tomasz, 2008. "A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-22, March.
    6. Goeman Jelle J, 2008. "Autocorrelated Logistic Ridge Regression for Prediction Based on Proteomics Spectra," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-12, February.
    7. Adrien Jamain & David Hand, 2008. "Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 87-112, June.
    8. Hoefsloot Huub C. J. & Smit Suzanne & Smilde Age K., 2008. "A Classification Model for the Leiden Proteomics Competition," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-11, February.
    9. Pham Thang V & van de Wiel Mark A & Jimenez Connie R, 2008. "Support Vector Machine Approach to Separate Control and Breast Cancer Serum Samples," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-11, February.
    10. Strimenopoulou Foteini & Brown Philip J, 2008. "Empirical Bayes Logistic Regression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-16, February.
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    Cited by:

    1. Blagus, Rok & Lusa, Lara, 2017. "Gradient boosting for high-dimensional prediction of rare events," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 19-37.
    2. D J Hand & F Zhou, 2010. "Evaluating models for classifying customers in retail banking collections," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1540-1547, October.
    3. Wang Chamont & Gevertz Jana L., 2016. "Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 321-347, August.

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