Data-Driven Extraction of Human Kinase-Substrate Relationships From Omics Datasets.

ABSTRACT: Phosphorylation forms an important part of the signaling system that cells use for decision making and regulation of processes such as cell division and differentiation. In human, >90% of identified phosphosites do not have annotations regarding the relevant upstream kinase. At the same time around 30% of kinases (as annotated in UniProt) have no known target. This knowledge gap stresses the need to make large-scale, data-driven computational predictions. In this study, we have created a machine learning-based model to derive a probabilistic kinase-substrate network from omics datasets. Our methodology displays improved performance compared to other state-of-the-art kinase-substrate prediction methods and provides predictions for more kinases. Importantly, it better captures new experiment

SUBMITTER: Maier BD 

PROVIDER: S-EPMC12359690 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature