{"id":"W3109055445","doi":"10.1002/cyto.b.21970","title":"Best practices for optimization and validation of flow cytometry‐based receptor occupancy assays","year":2020,"lang":"en","type":"article","venue":"Cytometry Part B Clinical Cytometry","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Caprion (Canada)","funders":"American Association of Pharmaceutical Scientists","keywords":"Flow cytometry; Computational biology; Occupancy; Computer science; Drug development; Identification (biology); Receptor; Drug; Biology; Pharmacology; Immunology; Biochemistry","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":[0.06286369,0.002785762,0.00228235,0.005516897,0.001794134,0.005028512,0.004831198,0.003720825,0.002789072],"category_scores_gemma":[0.06042012,0.00200575,0.00167112,0.002752231,0.003628046,0.002193648,0.003589594,0.004563306,0.005874142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002118659,"about_ca_system_score_gemma":0.004914843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002793303,"about_ca_topic_score_gemma":0.003636208,"domain_scores_codex":[0.9356089,0.02601924,0.005202818,0.007053178,0.02467838,0.001437575],"domain_scores_gemma":[0.9568095,0.01211359,0.003066873,0.009220734,0.01812026,0.000669127],"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":[0.0007016569,0.001050717,0.00741767,0.003021555,0.0005164066,0.0005846019,0.001580003,0.0194527,0.5869873,0.02053617,0.01741809,0.3407332],"study_design_scores_gemma":[0.0001495784,0.0009704192,0.004514524,0.001726179,0.0004398644,0.001025968,0.000709472,0.02949466,0.7620285,0.01808563,0.1804743,0.0003809987],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009001252,0.004849538,0.9731984,0.001665388,0.0005386129,0.002516374,0.0007208067,0.002827055,0.004682553],"genre_scores_gemma":[0.04729157,0.004802331,0.9391922,0.0008556078,0.0001863835,0.002952493,0.001594831,0.0007291782,0.002395428],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06286369,"threshold_uncertainty_score":0.332459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1555814999466496,"score_gpt":0.385775363086806,"score_spread":0.2301938631401564,"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."}}