Fig. 7: Cross-validation analysis for the 3D DESI MSI dataset of a human specimen of colorectal carcinoma.
From: Peak learning of mass spectrometry imaging data using artificial neural networks
a The training set was used to optimize the neural network and then the trained model was applied on the testing set, and this process was repeated three times (rows) according to three-fold cross-validation shown in (b) in which the full dataset was randomly shuffled and split into training and testing sets. There is a close consensus in the performance of the cross-validated models in predicting the original data, learning the nonlinear manifold, and identifying the tumor and normal clusters. c The three cross-validated models showed stability in learning peaks of interest such as m/z 279.2 and m/z 766.5 that were found localized (>0.7 Pearson correlation) and elevated in the tumor and normal clusters, respectively.
