Figure 3.

Schematic overview of classification approach along with integration strategies. The left part shows no and early integration strategies by training the logistic regression classifier on only GEP or DMP and GEP+DMP, respectively. In the DLCV scheme we split the input data into five subsets. The classifier was trained by means of a 5-fold cross-validation approach using four subsets for training and testing and one for validation. The right part shows the late integration procedure where the nearest mean classifier (NMC) (i.e. second layer) was trained on the new two-dimensional data which represents the first layer outcomes for GEP and DMP sets. The second layer was evaluated by the first layer outcomes of the validation subset. The reported performance is the average of classification performance on the 5 validation subsets.