{"id":"W2791965829","doi":"10.3389/fimmu.2018.00460","title":"Strategies for Generating Diverse Antibody Repertoires Using Transgenic Animals Expressing Human Antibodies","year":2018,"lang":"en","type":"article","venue":"Frontiers in Immunology","topic":"Viral Infectious Diseases and Gene Expression in Insects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Amgen (Canada)","funders":"","keywords":"Antibody; Antibody Repertoire; Transgene; Immune system; Monoclonal antibody; Genetically modified mouse; Biology; Immunology; Human disease; Computational biology; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008887618,0.0005017098,0.0003078522,0.0004538545,0.0003100286,0.0007108872,0.000443338,0.0005167557,0.001471175],"category_scores_gemma":[0.0002854287,0.0003391445,0.0003860932,0.0002466005,0.0006057148,0.0004582373,0.0005826292,0.001366081,0.0006445121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003352335,"about_ca_system_score_gemma":0.0002408207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002124236,"about_ca_topic_score_gemma":0.0005208215,"domain_scores_codex":[0.99965,0.00008869727,0.00003098726,0.00006344049,0.000112802,0.00005405764],"domain_scores_gemma":[0.9998368,0.00004206995,0.00005008486,0.00002797152,0.0000164569,0.00002664201],"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":[0.00004122982,0.00005280807,0.000192195,0.0001579818,0.00001641879,0.0001156426,0.00008573681,0.000385343,0.9802787,0.006726516,0.0004334966,0.01151379],"study_design_scores_gemma":[0.00008231801,0.0009082523,0.001332196,0.00009428701,0.00009704364,0.001421189,0.00008159807,0.002866355,0.893431,0.002892926,0.09674584,0.00004692173],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2897096,0.01213932,0.6711473,0.00192107,0.0004281042,0.001068537,0.0008032981,0.002026736,0.02075606],"genre_scores_gemma":[0.5861138,0.01538691,0.3847445,0.001189159,0.0001360882,0.000991025,0.001239013,0.0004143048,0.00978526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001471175,"threshold_uncertainty_score":0.004921556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02278874392716572,"score_gpt":0.3210499648148442,"score_spread":0.2982612208876784,"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."}}