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A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry

Author

Listed:
  • Valkenborg Dirk

    (Hasselt University, Center for Statistics)

  • Van Sanden Suzy

    (Hasselt University, Center for Statistics)

  • Lin Dan

    (Hasselt University, Center for Statistics)

  • Kasim Adetayo

    (Hasselt University, Center for Statistics)

  • Zhu Qi

    (Hasselt University, Center for Statistics)

  • Haldermans Philippe

    (Hasselt University, Center for Statistics)

  • Jansen Ivy

    (Hasselt University, Center for Statistics)

  • Shkedy Ziv

    (Hasselt University, Center for Statistics)

  • Burzykowski Tomasz

    (Hasselt University, Center for Statistics)

Abstract

We present an approach to construct a classification rule based on the mass spectrometry data provided by the organizers of the "Classification Competition on Clinical Mass Spectrometry Proteomic Diagnosis Data." Before constructing a classification rule, we attempted to pre-process the data and to select features of the spectra that were likely due to true biological signals (i.e., peptides/proteins). As a result, we selected a set of 92 features. To construct the classification rule, we considered eight methods for selecting a subset of the features, combined with seven classification methods. The performance of the resulting 56 combinations was evaluated by using a cross-validation procedure with 1000 re-sampled data sets. The best result, as indicated by the lowest overall misclassification rate, was obtained by using the whole set of 92 features as the input for a support-vector machine (SVM) with a linear kernel. This method was therefore used to construct the classification rule. For the training data set, the total error rate for the classification rule, as estimated by using leave-one-out cross-validation, was equal to 0.16, with the sensitivity and specificity equal to 0.87 and 0.82, respectively.

Suggested Citation

  • 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.
  • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:2:n:12
    DOI: 10.2202/1544-6115.1363
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    References listed on IDEAS

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    1. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
    2. Lee, Jae Won & Lee, Jung Bok & Park, Mira & Song, Seuck Heun, 2005. "An extensive comparison of recent classification tools applied to microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 869-885, April.
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    Cited by:

    1. 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.

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