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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8049

Title: A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry
Authors: VALKENBORG, Dirk
VAN SANDEN, Suzy
LIN, Dan
KASIM, Adetayo
JANSEN, Ivy
SHKEDY, Ziv
BURZYKOWSKI, Tomasz
HALDERMANS, Philippe
ZHU, Qi
Issue Date: 2008
Publisher: BERKELEY ELECTRONIC PRESS
Citation: STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 7(2)
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.
URI: http://hdl.handle.net/1942/8049
ISI #: 000254568100009
ISSN: 1544-6115
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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