Fig. 3: Convolutional neural network identifies non-linear signal transmission.
From: Deciphering the history of ERK activity from fixed-cell immunofluorescence measurements
a For each ETG, three types of prediction models were separately trained. Top: Simplified schematic of convolutional neural network architecture containing two convolutional layers and three fully connected layers. Bottom left: Multiple variable regression where ERK activity at each time point is considered as a predictor variable (TS linear). Bottom right: Multiple variable linear regression where nine features of ERK activity are considered as predictor variables (Featurized linear). b Top: Bar plot indicating R2 for three models used to predict ETG levels. Bottom: Bar plot indicating mean square error for three models used to predict ETG levels. Error bars represent standard error calculated using values from each fold of the 5-fold cross-validation partitions. c Scatter plot of the predicted and observed values of the CNN trained on all 190 timepoints (19 hr). The data represent standardized (z-scored) values. d Feature attribution heatmap showing the importance of each timepoint in the CNN model trained on 150 timepoints (15 h). Colormap represents relative values within each row. c and d represent the validation set of the first 5-fold partition.
