{"doi":"10.1093/bioinformatics/btaf349","title":"Inference of differential kinase interaction networks with KINference","abstract":"MOTIVATION: Differential kinase interaction networks (DKINs) are networks containing kinase-substrate links that are differentially active between two conditions. Existing methods are either able to predict condition-agnostic kinase-substrate links or condition-specific differential kinase activity, but do not provide differential kinase-substrate links. Moreover, existing methods for predicting kinase-substrate links usually rely on curated biochemical knowledge. Thus, there is a lack of data-driven DKIN inference methods that are also applicable when prior knowledge is scarce. RESULTS: To address this need, we present KINference. KINference combines computation of a baseline KIN representing the space of all possible kinase-substrate links with filters applied to nodes and edges to identify differentially active subnetworks that are relevant in the context of a specific phosphoproteomics dataset. For the node filters, we rely on functional relevance and differential phosphorylation scores; for the edge filters, we make use of prize-collecting Steiner trees and correlations between phosphorylation sites of kinases and their target proteins. Tests on two phosphoproteomics datasets (kinase inhibition in breast cancer cells, SARS-CoV-2 infection in Calu-3 cells) show that the proposed filters produce significant results in terms of overlap with known interactions between kinases and phosphorylation sites. Furthermore, a case study on the SARS-CoV-2 infection data, suggests a potential host pathway linked to virus replication, showcasing the process of hypothesis generation utilizing DKINs computed by KINference. AVAILABILITY AND IMPLEMENTATION: KINference is available as an R package at https://github.com/bionetslab/KINference and https://doi.org/10.5281/zenodo.15411150. Scripts to reproduce the results are available at https://github.com/bionetslab/KINference-Evaluation-Scripts and https://doi.org/10.5281/zenodo.15424599.","journal":"Bioinformatics","year":2025,"id":539665,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":3,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9552,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1363,"name":"Nevan J. Krogan","orcid":"0000-0003-4902-337X","position":1,"is_corresponding":false},{"id":1458,"name":"Benjamin J. Polacco","orcid":"0000-0003-1570-9234","position":2,"is_corresponding":false},{"id":85201,"name":"David B. Blumenthal","orcid":"0000-0001-8651-750X","position":3,"is_corresponding":false},{"id":1427598,"name":"Nicolai Meyerhöfer","orcid":null,"position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:52:34.520788Z","pmid":"40579228","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}