{"id":"W4408216417","doi":"10.1021/acs.jproteome.4c00931","title":"Coupling Proximity Biotinylation with Genomic Targeting to Characterize Locus-Specific Changes in Chromatin Environments","year":2025,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; PROTEO; Centre hospitalier universitaire de Québec","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Centre Hospitalier Universitaire de Québec; Canada Foundation for Innovation; Fondation CHU de Québec; Cancer Research Society; Université Laval","keywords":"Biotinylation; Chromatin; Computational biology; Locus (genetics); Biology; Coupling (piping); Genetics; Cell biology; Chemistry; Molecular biology; DNA; Gene; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001967437,0.0003412565,0.0002130124,0.0003097101,0.0002317494,0.0004179763,0.0003076105,0.000473929,0.002457504],"category_scores_gemma":[0.0002256206,0.000246717,0.0002271713,0.0002153823,0.0003069042,0.0003822646,0.0003844496,0.0007592234,0.0008004298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003352852,"about_ca_system_score_gemma":0.0002073023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004778531,"about_ca_topic_score_gemma":0.001334857,"domain_scores_codex":[0.9998273,0.00001707215,0.00000941627,0.00007532171,0.00004187853,0.00002900354],"domain_scores_gemma":[0.9997956,0.00007607204,0.00005319067,0.00002666699,0.00001979749,0.00002875273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000118704,0.000003430782,0.00006070418,0.00002048802,0.000001768086,0.00001444025,0.000007123568,0.00002425789,0.9991702,0.00004513045,0.00001485581,0.0006256893],"study_design_scores_gemma":[0.000003080924,0.00003457611,0.001994529,0.000002719331,0.000004164254,0.0001204876,0.00001569538,0.0007054193,0.9958222,0.00005923702,0.001233934,0.000003958457],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8386449,0.001973334,0.1526857,0.0003822907,0.00009578464,0.0001104712,0.001183598,0.0009868874,0.003937053],"genre_scores_gemma":[0.8825755,0.001941721,0.1069077,0.0002115618,0.00002849467,0.0002227831,0.001452856,0.000325486,0.006334023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002457504,"threshold_uncertainty_score":0.008221149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03095341637473516,"score_gpt":0.3078151686165008,"score_spread":0.2768617522417657,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}