{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["24(8)"],"submitter":["Maier BD"],"funding":["European Bioinformatics Institute","European Molecular Biology Laboratory"],"pubmed_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"],"journal":["Molecular & cellular proteomics : MCP"],"pagination":["100994"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12359690"],"repository":["biostudies-literature"],"pubmed_title":["Data-Driven Extraction of Human Kinase-Substrate Relationships From Omics Datasets."],"pmcid":["PMC12359690"],"pubmed_authors":["Lussana A","Petursson B","Maier BD","Petsalaki E"],"additional_accession":[]},"is_claimable":false,"name":"Data-Driven Extraction of Human Kinase-Substrate Relationships From Omics Datasets.","description":"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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 May","modification":"2026-05-29T17:08:41.532Z","creation":"2026-04-08T05:28:06.061Z"},"accession":"S-EPMC12359690","cross_references":{"pubmed":["40381888"],"doi":["10.1016/j.mcpro.2025.100994"]}}