Table 2.
Metric | In Sample | Out Sample | 95 % Confidence Interval | |
---|---|---|---|---|
Metabolites | R2 | 0.997 | 0.840 | [ 0.335, 0.972 ] |
Mean Absolute Error | 0.228 | 2.503 | [ 0.783, 4.060 ] | |
Mean Squared Error | 0.2104 | 12.180 | [ 0.964, 24.916 ] | |
Transcription factors | R2 | 0.983 | 0.825 | [ 0.565, 0.939 ] |
Mean Absolute Error | 0.897 | 2.658 | [ 1.009, 4.950 ] | |
Mean Squared Error | 1.239 | 13.315 | [ 3.743, 29.804 ] | |
Integrated list | R2 | 0.9965 | 0.909 | [ 0.721, 0.986 ] |
Mean Absolute Error | 0.465 | 1.779 | [ 0.487, 3.919 ] | |
Mean Squared Error | 0.2639 | 6.927 | [ 0.469, 18.832 ] |
Performances are reported in terms of the determination coefficient R2, Mean Absolute Error (MAE) and Mean Squared Error (MSE, see text for more details on these metrics). The in-sample performances quantify the fitness of the predictive models on the training data, while the out-of-sample values estimate the expected performance on new data. Confidence intervals are calculated using a bootstrap approach