4.1 Approaches for CBM and ML integration
The integration of ML and CBM can be conducted in three chief ways [26]: (a) The output of CBM is the flux distribution that indicates the metabolic state of the cell. This fluxomic data can be trained directly through ML methods to obtain more biological insight into the desired system (Figure 5A ). (b) ML is an effective tool for merging and analyzing heterogeneous omics datasets beyond ML applications to single omics. By combining these multi-omics datasets with GEMs, context-specific models are generated. More accurate flux values obtained from context-specific GEMs can be re-integrated with experimental omics data for further predictions (Figure 5B ). (c) CBM models and fluxomic data can be produced directly by introducing omics or multi-omics datasets into ML algorithms. All of the three mentioned methods might be operated by supervised or unsupervised algorithms (Figure 5C ).