A cross-validation study to select a classification procedure for clinical diagnosis based on proteomic mass spectrometry.
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.
Valkenborg
Dirk
D
Hasselt University, Center for Statistics. dirk.valkenborg@uhasselt.be
Van Sanden
Suzy
S
Lin
Dan
D
Kasim
Adetayo
A
Zhu
Qi
Q
Haldermans
Philippe
P
Jansen
Ivy
I
Shkedy
Ziv
Z
Burzykowski
Tomasz
T
D003933:Diagnosis; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D040901:Proteomics; D012680:Sensitivity and Specificity