{"id":"W2196145446","doi":"10.1371/journal.pone.0139695","title":"Optimizing Production of Antigens and Fabs in the Context of Generating Recombinant Antibodies to Human Proteins","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; Structural Genomics Consortium; University of Toronto","funders":"Common Fund; National Institute of General Medical Sciences; National Institutes of Health; Ministero dello Sviluppo Economico; Genome Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo; Ontario Ministry of Economic Development and Innovation; Innovative Medicines Initiative; Canadian Institutes of Health Research; National Human Genome Research Institute; Wellcome Trust; GlaxoSmithKline; Pfizer; Eli Lilly and Company","keywords":"Biotinylation; Recombinant DNA; Antigen; Antibody; Phage display; Context (archaeology); Biology; In vivo; Immunoprecipitation; Molecular biology; Computational biology; Chemistry; Biochemistry; Gene; Biotechnology; Genetics","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":[0.002391673,0.001006337,0.0008320961,0.0006882721,0.0003394383,0.001136685,0.0008225247,0.0005624464,0.0009309838],"category_scores_gemma":[0.001707917,0.0004736416,0.0007508211,0.000700585,0.000341927,0.0005518282,0.0005901789,0.001065288,0.001432138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006967577,"about_ca_system_score_gemma":0.0008670605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001143336,"about_ca_topic_score_gemma":0.002171312,"domain_scores_codex":[0.9987416,0.0002363179,0.0001247364,0.0002082787,0.0004994671,0.000189667],"domain_scores_gemma":[0.9994111,0.0001604558,0.00008604302,0.00009242352,0.0001932837,0.00005673494],"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":[0.00005799319,0.00004545011,0.0003929995,0.0001338662,0.00002424808,0.00009097752,0.00004801591,0.00147524,0.9904135,0.0002566446,0.0001804878,0.006880635],"study_design_scores_gemma":[0.000009117406,0.00008151169,0.0006845078,0.00001053913,0.00002706,0.0001794189,0.00001957781,0.002025265,0.9920292,0.0001127233,0.004807217,0.00001377292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5808825,0.003016523,0.4068905,0.0003630379,0.0001176012,0.001069516,0.002083473,0.002141184,0.003435602],"genre_scores_gemma":[0.518379,0.003498055,0.4671899,0.000220465,0.00004095735,0.0005605352,0.004836676,0.000832886,0.004441514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002391673,"threshold_uncertainty_score":0.01264846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1497397666051908,"score_gpt":0.3298298427271941,"score_spread":0.1800900761220033,"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."}}