{"id":"W2963604513","doi":"10.1101/712539","title":"Parameters and determinants of responses to selection in antibody libraries","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Phage display; Selection (genetic algorithm); Antibody; Antigen; Affinity maturation; Repertoire; Antibody response; Antibody Repertoire; Computational biology; Biology; Positive selection; Negative selection; Evolutionary biology; Genetics; Computer science; Genome; Artificial intelligence; Gene; Physics","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":[0.001550441,0.0003536934,0.0004455494,0.0006752679,0.0001415066,0.0008152614,0.0003886246,0.000525539,0.001201871],"category_scores_gemma":[0.007649196,0.000192071,0.0001983615,0.0004154853,0.0004697866,0.0004316731,0.0004943947,0.0005857504,0.0001673274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006156291,"about_ca_system_score_gemma":0.0001463619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002401155,"about_ca_topic_score_gemma":0.0001072853,"domain_scores_codex":[0.9985511,0.000559565,0.00009106183,0.0002325139,0.0003832472,0.0001824607],"domain_scores_gemma":[0.9923748,0.005453763,0.001013612,0.0003670346,0.0004815746,0.0003092277],"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":[0.0005379469,0.0001375642,0.007837067,0.00008235298,0.0000480714,0.00004740983,0.00005606652,0.01420781,0.9713758,0.0005727938,0.00006549717,0.005031632],"study_design_scores_gemma":[0.00004264746,0.001233995,0.02626649,0.00001027169,0.00004355169,0.0001944702,0.00008252939,0.06020536,0.9106486,0.0008822966,0.0003432629,0.00004647461],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945498,0.00008651087,0.004698005,0.00003909709,0.000004350909,0.00001831165,0.00009843492,0.00007257181,0.0004328738],"genre_scores_gemma":[0.9987304,0.00002868303,0.0009607956,0.00002310673,0.000002762604,0.00001902053,0.00009832332,0.00001345163,0.0001235169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001550441,"threshold_uncertainty_score":0.008199573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0275601577094946,"score_gpt":0.300060120787912,"score_spread":0.2724999630784174,"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."}}