{"id":"W4206302690","doi":"10.1093/nar/gkac045","title":"Barcode fusion genetics-protein-fragment complementation assay (BFG-PCA): tools and resources that expand the potential for binary protein interaction discovery","year":2022,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"PROTEO; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Watanabe Foundation; Astellas Foundation for Research on Metabolic Disorders; Daiichi Sankyo Foundation of Life Science; Ube Foundation; Japan Society for the Promotion of Science; Sylff Association; Canada Research Chairs","keywords":"Biology; Barcode; Genetics; Complementation; Computational biology; Fusion protein; Fragment (logic); Protein-fragment complementation assay; Gene; Phenotype; Computer science; Operating system; Programming language","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":[0.002476866,0.001573293,0.00166647,0.002492172,0.0006519843,0.001654448,0.001766987,0.001488023,0.003055371],"category_scores_gemma":[0.002592227,0.0009000782,0.00121844,0.001780539,0.0008946809,0.001212084,0.002411054,0.0023827,0.003701208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008499954,"about_ca_system_score_gemma":0.0009935924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001371565,"about_ca_topic_score_gemma":0.002561125,"domain_scores_codex":[0.9980236,0.000341073,0.0001238864,0.0003969153,0.0009459911,0.0001685541],"domain_scores_gemma":[0.9980045,0.0007660236,0.0004413109,0.0003921609,0.000194993,0.0002010677],"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":[0.0002434354,0.0002028423,0.001822936,0.0004580365,0.0001145594,0.0001941419,0.00008524841,0.001013866,0.9520586,0.002745803,0.004094274,0.03696628],"study_design_scores_gemma":[0.00006677394,0.0002736875,0.003650413,0.00006951143,0.00009141035,0.001176958,0.00004458458,0.01156099,0.9400029,0.002066427,0.04087353,0.000122759],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1120335,0.002714233,0.8305475,0.001024589,0.0002417622,0.0008834889,0.01424822,0.03256889,0.005737858],"genre_scores_gemma":[0.1998138,0.003158177,0.7591667,0.0007452188,0.00006505837,0.001338465,0.02592653,0.003451407,0.006334594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003055371,"threshold_uncertainty_score":0.01309907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04017232209720175,"score_gpt":0.3199505771097271,"score_spread":0.2797782550125254,"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."}}