{"id":"W3011155739","doi":"10.1126/sciadv.aax2271","title":"An array of 60,000 antibodies for proteome-scale antibody generation and target discovery","year":2020,"lang":"en","type":"article","venue":"Science Advances","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"National Natural Science Foundation of China","keywords":"Antibody; Monoclonal antibody; Computational biology; Proteome; Massively parallel; Computer science; Biology; Immunology; Bioinformatics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000280153,0.0001036468,0.0002310926,0.00007919121,0.000226275,0.00005638594,0.0001589963,0.00002325858,0.00001802949],"category_scores_gemma":[0.0001279614,0.00007258628,0.0000404681,0.0003331713,0.0009133948,0.00117292,0.00003609984,0.0000710455,0.000001809244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009001186,"about_ca_system_score_gemma":0.0001327417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001955146,"about_ca_topic_score_gemma":0.000006921863,"domain_scores_codex":[0.9987196,0.00001609831,0.0001884381,0.0003735459,0.0004306397,0.0002717005],"domain_scores_gemma":[0.999408,0.00003369307,0.00006766673,0.0001266703,0.0001705041,0.0001934473],"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":[0.0001778865,0.00004726192,0.04475121,0.0001979942,0.000004874368,0.000002139086,0.0003934832,0.00004938163,0.9505264,0.0001797436,0.0000312944,0.003638369],"study_design_scores_gemma":[0.0003592681,0.001255857,0.03020526,0.00004512702,0.00001067435,0.000007413882,0.0003375908,0.003261607,0.9589255,0.0003870043,0.005084881,0.0001197709],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864728,0.001312906,0.008692104,0.002375927,0.00008659218,0.0005247989,0.00008464717,0.00001825878,0.0004319297],"genre_scores_gemma":[0.9708731,0.0002920627,0.02791244,0.0003018682,0.0003038083,0.00001931991,0.00004220432,0.000007260471,0.0002479317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01922034,"threshold_uncertainty_score":0.3365441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03399925047679037,"score_gpt":0.3663735227649538,"score_spread":0.3323742722881635,"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."}}