Accurate prediction of kinase-substrate networks using knowledge graphs
Fig 2
The model is first trained on phosphorylation network data that has been converted to a knowledge graph representation.
Such a representation can be readily processed by link prediction algorithms (contrary to the original phosphorylation data). In the training stage, an optimal combination of model parameters is found and computationally validated. The optimal model is then trained on full phosphorylation network data and used for providing probabilistic ranking scores for all possible predictions that can be made using the input. Finally, reverse conversion technique is applied to the computed predictions to present them to users as residue-specific kinase-substrate relationships.