{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["17(1)"],"submitter":["Garrido-Rodriguez M"],"pubmed_abstract":["Signaling pathways are useful models for interpreting molecular data, but their coverage has long been constrained by classic biochemistry methods. The growing corpus of kinase-substrate interactions, coupled to phosphoproteomics improvements, pave the way to revisit classic signaling pathways. In this study, we explore context-specific signaling pathway inference from phosphoproteomics and kinase-substrate networks. Focusing on epidermal growth factor (EGF), we conduct a meta-analysis and generate three datasets representing the most comprehensive characterization of the EGF response to date. We infer kinase-kinase pathways and compare them to different ground truth sets. Literature-curated networks consistently yield the highest recovery of ground-truth interactions, with modest gains fr"],"journal":["Nature communications"],"pagination":["2071"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12949236"],"repository":["biostudies-literature"],"pubmed_title":["Benchmarking EGF signaling pathway inference using phosphoproteomics and kinase-substrate interactions."],"pmcid":["PMC12949236"],"pubmed_authors":["Potel C","Garrido-Rodriguez M","Becher I","Savitski MM","Saez-Rodriguez J","Rodriguez-Mier P","Burtscher ML","Muller-Dott S"],"additional_accession":[]},"is_claimable":false,"name":"Benchmarking EGF signaling pathway inference using phosphoproteomics and kinase-substrate interactions.","description":"Signaling pathways are useful models for interpreting molecular data, but their coverage has long been constrained by classic biochemistry methods. The growing corpus of kinase-substrate interactions, coupled to phosphoproteomics improvements, pave the way to revisit classic signaling pathways. In this study, we explore context-specific signaling pathway inference from phosphoproteomics and kinase-substrate networks. Focusing on epidermal growth factor (EGF), we conduct a meta-analysis and generate three datasets representing the most comprehensive characterization of the EGF response to date. We infer kinase-kinase pathways and compare them to different ground truth sets. Literature-curated networks consistently yield the highest recovery of ground-truth interactions, with modest gains fr","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-07-16T23:25:40.786Z","creation":"2026-07-12T03:11:21.577Z"},"accession":"S-EPMC12949236","cross_references":{"pubmed":["41741430"],"doi":["10.1038/s41467-026-69332-0"]}}