Figure 2: Machine learning workflow.
Figure 3: An overview of the structure of a dataset. This figure demonstrates a labeled dataset because one or more target values are reported. Datasets are usually prepared by data scientists. However, the more one knows about a dataset, the easier the machine learning process will be.
Figure 4: The role of cross-validation in machine learning.First, the generalization power of the ML model is evaluated through cross-validation. Then, hyperparameter tuning is performed before training the model to refine the model’s parameters which are called hyperparameters. Grid search and Bayesian optimization algorithms are the most common search-based methods to tune hyperparameters. Finally, the curated model is used to evaluate the prediction capability of unseen test data.
Figure 5: Integrating constraint-based modeling and machine learning. CBM and ML can be integrated in different ways for analysis and optimization of fermentation parameters. (a) Predicting parameters by ML when fluxomics are used as inputs. (b)Predicting parameters by ML when the integration of fluxomics with multi-omics is used as input. (c) Predicting fluxomics and constraint-based models by ML when multi-omics are used as inputs.