{"id":"W3039043954","doi":"10.1021/acs.jproteome.0c00117","title":"Variability in Streptavidin–Sepharose Matrix Quality Can Significantly Affect Proximity-Dependent Biotinylation (BioID) Data","year":2020,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Biotin and Related Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Sinai Health System; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Institute of Genetics; Ontario Ministry of Health and Long-Term Care; Canada Foundation for Innovation; Government of Ontario; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Princess Margaret Cancer Foundation","keywords":"Biotinylation; Streptavidin; Sepharose; Matrix (chemical analysis); Affect (linguistics); Biotin; Computational biology; Chemistry; Biology; Computer science; Molecular biology; Chromatography; Biochemistry; Enzyme; Psychology; Communication","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":[0.009823703,0.001383081,0.001177359,0.001634536,0.001293505,0.003248868,0.001027046,0.001021133,0.0018045],"category_scores_gemma":[0.01885589,0.0008471567,0.0008392472,0.001826843,0.001634185,0.0007686572,0.001932684,0.002328354,0.00125429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009381347,"about_ca_system_score_gemma":0.0007808532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001452051,"about_ca_topic_score_gemma":0.002371445,"domain_scores_codex":[0.982857,0.004241215,0.002342335,0.002632289,0.007032687,0.0008944078],"domain_scores_gemma":[0.9873742,0.006208795,0.001568819,0.002420703,0.002139603,0.0002879039],"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":[0.0003619351,0.00008909582,0.003955681,0.0003624528,0.0001433795,0.0001604351,0.000408832,0.0003109056,0.9853361,0.0003248108,0.0003637216,0.008182585],"study_design_scores_gemma":[0.000009503452,0.0001390812,0.01085486,0.00003434612,0.00007095984,0.000369276,0.0001334333,0.001615442,0.9827955,0.000385822,0.003563528,0.00002819518],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7299286,0.005767042,0.2471995,0.001527754,0.0007551443,0.0006169844,0.004714949,0.002209175,0.007280844],"genre_scores_gemma":[0.8767293,0.002325185,0.1067,0.001110269,0.00006200746,0.0006291307,0.006967771,0.001270137,0.004206163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009823703,"threshold_uncertainty_score":0.05195338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1919264457659229,"score_gpt":0.4486066949395477,"score_spread":0.2566802491736249,"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."}}