{"id":"W2803787116","doi":"10.1177/2472555218774334","title":"Flow Cytometry-Based Epitope Binning Using Competitive Binding Profiles for the Characterization of Monoclonal Antibodies against Cellular and Soluble Protein Targets","year":2018,"lang":"en","type":"article","venue":"SLAS DISCOVERY","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Amgen (Canada); Burnaby Hospital","funders":"Amgen Canada; Amgen","keywords":"Epitope; Antibody; Monoclonal antibody; Multiplex; Computational biology; Epitope mapping; Flow cytometry; Linear epitope; Antigen; Biology; Molecular biology; Chemistry; Immunology; Bioinformatics","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":[0.001159176,0.0005680759,0.0005180474,0.002023026,0.0004650619,0.0008779023,0.0006654232,0.0005103888,0.001180096],"category_scores_gemma":[0.001400448,0.0002683081,0.0003709734,0.001310419,0.0004317895,0.0008375013,0.0005427455,0.0009950711,0.000484994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001045729,"about_ca_system_score_gemma":0.000596391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001842569,"about_ca_topic_score_gemma":0.002737451,"domain_scores_codex":[0.9992981,0.0001202913,0.00003438357,0.00017435,0.0002770018,0.00009585998],"domain_scores_gemma":[0.9994036,0.000255931,0.00009545135,0.00005653586,0.0001328996,0.00005560916],"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":[0.0002524453,0.00009474238,0.002223117,0.0001167543,0.00004177757,0.00002554708,0.00008269932,0.003206228,0.9437718,0.003508271,0.0005885935,0.04608802],"study_design_scores_gemma":[0.0000302558,0.0001561654,0.005759808,0.00001978876,0.00004472891,0.0001940769,0.00004676271,0.101794,0.8839443,0.002796601,0.005150924,0.0000627023],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2776703,0.002629082,0.7132231,0.0002897174,0.00007402297,0.000324788,0.001018998,0.002200714,0.002569273],"genre_scores_gemma":[0.5775402,0.001561979,0.4170198,0.0003681637,0.00004701663,0.0005137162,0.001400847,0.0001338672,0.001414422],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002023026,"threshold_uncertainty_score":0.007587373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03419203038745667,"score_gpt":0.3032009490786303,"score_spread":0.2690089186911736,"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."}}