biostudies-literatureUnknown24(8)Maier BDEuropean Bioinformatics InstituteEuropean Molecular Biology LaboratoryPhosphorylation 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 experimentMolecular & cellular proteomics : MCP100994https://www.ebi.ac.uk/biostudies/studies/S-EPMC12359690biostudies-literatureData-Driven Extraction of Human Kinase-Substrate Relationships From Omics Datasets.PMC12359690Lussana APetursson BMaier BDPetsalaki EfalseData-Driven Extraction of Human Kinase-Substrate Relationships From Omics Datasets.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 experiment2025-01-01T00:00:00Z2025 May2026-05-29T17:08:41.532Z2026-04-08T05:28:06.061ZS-EPMC123596904038188810.1016/j.mcpro.2025.100994