{"id":"W2087904752","doi":"10.1038/nmeth.3170","title":"A robust pipeline for rapid production of versatile nanobody repertoires","year":2014,"lang":"en","type":"article","venue":"Nature Methods","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":535,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute","funders":"National Institute of General Medical Sciences; Fonds de Recherche du Québec - Santé; National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health; Howard Hughes Medical Institute","keywords":"Single-domain antibody; Computational biology; Epitope; Phage display; Recombinant DNA; Protein engineering; Antigen; Biology; Antibody; Chemistry; Molecular biology; Biochemistry; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001909671,0.0001261736,0.0003651723,0.0001336162,0.00007515527,0.00000562602,0.0001006271,0.0002380339,0.0001260522],"category_scores_gemma":[0.003927462,0.000087934,0.000172194,0.0002314104,0.0001022239,0.00003986104,0.00003882448,0.0004252575,0.000003460794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002373127,"about_ca_system_score_gemma":0.00006612118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002835547,"about_ca_topic_score_gemma":0.000004447691,"domain_scores_codex":[0.9986787,0.0002360505,0.0002592198,0.0003019927,0.0002986184,0.0002253954],"domain_scores_gemma":[0.9985523,0.000545929,0.00008793817,0.0003254491,0.0003967649,0.00009160602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002123674,0.0001969134,0.0007178747,0.0007755858,0.0001020133,0.000002297738,0.0001914258,0.00002685641,0.2985973,0.002363031,0.0463555,0.6485475],"study_design_scores_gemma":[0.000843073,0.0004938154,0.001972071,0.000107295,0.00008043247,0.00004180457,0.00003911717,0.006154033,0.4016116,0.0008285744,0.5877129,0.0001152623],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.333354,0.02761878,0.4862828,0.07747792,0.01037951,0.006478397,0.0001261694,0.0004638495,0.05781859],"genre_scores_gemma":[0.1297061,0.00019394,0.8444883,0.0006830426,0.001738672,0.00005214086,0.00008622222,0.00003441597,0.0230172],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6484322,"threshold_uncertainty_score":0.4701823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05483255117589513,"score_gpt":0.4154840297407529,"score_spread":0.3606514785648577,"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."}}