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Table 2 Average performance (F1, accuracy, precision, recall) of the different models in a supervised task scenario. Although our pathway-primed models are nearly ten times smaller (sparse), the performance is very close to the PPI-based NN. We report the mean for 100 iterations of train test splits

From: Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data

  F1 PRECISION RECALL
Architecture Number of nodes
(2nd hidden layer)
Accuracy Balanced accuracy Macro Micro Weighted Macro Micro Weighted Macro Micro Weighted
Dense 0.825 0.788 0.748 0.825 0.802 0.769 0.825 0.844 0.788 0.825 0.825
Dense with pathways 0.810 0.781 0.743 0.810 0.783 0.763 0.810 0.823 0.781 0.810 0.810
Dense with PPI 0.802 0.770 0.730 0.802 0.774 0.753 0.802 0.817 0.770 0.802 0.802
Dense with PPI/GRN 0.800 0.777 0.735 0.800 0.771 0.757 0.800 0.815 0.777 0.800 0.800
Signaling pathways 0.813 0.781 0.743 0.813 0.790 0.764 0.813 0.834 0.781 0.813 0.813
100 0.766 0.724 0.673 0.766 0.728 0.690 0.766 0.762 0.724 0.766 0.766