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.

Submitted: February 11, 2008 · Accepted: February 20, 2008 · Published: March 24, 2008

Recommended Citation

Valkenborg, Dirk; Van Sanden, Suzy; Lin, Dan; Kasim, Adetayo; Zhu, Qi; Haldermans, Philippe; Jansen, Ivy; Shkedy, Ziv; and 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: Vol. 7 : Iss. 2, Article 12.
Available at: http://www.bepress.com/sagmb/vol7/iss2/art12