teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering
Fig 5
Learning curves and top-ranking strains designs from the iterative engineering cycles.
Learning curves from the first (A) and second (B) DBTL cycles, illustrating mean absolute error (MAE) of the best-performing deep learning and XGBoost models used cycle I and II, respectively, in relation to the number of data points (blue line) and the cross-validation holdout prediction MAE together with the standard deviations of the 10 models created (yellow line). The points are based on 10 models created with a randomized shuffled data in partitions of 33, 67, 100% and 20, 40, 60, 80 and 100% of the data available for dbtl1 and dbtl2 respectively to get the same size of partitions. (C) Average strictosidine production for Top-20 strains from first and second DBTL cycles. Genotypes are shown (left) with their respective color codes (middle) and average strictosidine production (right). For the strictosidine production, the light and dark blue colors correspond to strain designs that were first found in the first and second second DBTL cycle, respectively.