{"id":"W3000835232","doi":"10.1038/s42003-020-0765-z","title":"A normalized drug response metric improves accuracy and consistency of anticancer drug sensitivity quantification in cell-based screening","year":2020,"lang":"en","type":"article","venue":"Communications Biology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biocenter Finland; Syöpäjärjestöt; University of Toronto; Pancreatic Cancer Action Network; Academy of Finland; Helsingin Yliopisto; Sigrid Juséliuksen Säätiö","keywords":"Drug; Sensitivity (control systems); Metric (unit); Anticancer drug; Drug response; Consistency (knowledge bases); Pharmacology; Medicine; Computer science; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009362669,0.0001185422,0.0002478537,0.000169187,0.00008094814,0.00001198175,0.000347362,0.00009026665,0.000003270461],"category_scores_gemma":[0.001454744,0.0001219013,0.00007403987,0.0004564729,0.0004180076,0.000008338512,0.0003605122,0.0001376359,0.000001243878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009774452,"about_ca_system_score_gemma":0.00009686018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002683207,"about_ca_topic_score_gemma":0.00023739,"domain_scores_codex":[0.9980811,0.001070281,0.0003726429,0.0002943975,0.00004604573,0.0001355397],"domain_scores_gemma":[0.9978456,0.0006059474,0.0002438258,0.001091122,0.0001633452,0.00005015268],"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":[0.0002666014,0.00008119703,0.01577565,0.00001907073,0.00002104003,4.988779e-7,0.0001167836,0.00001265833,0.980104,0.00004900771,0.0001713715,0.003382146],"study_design_scores_gemma":[0.0007389663,0.0001074179,0.01804858,0.00001545514,0.00005505357,0.000002064742,0.0002579808,0.01398272,0.961362,0.00003524728,0.005191959,0.0002025376],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755269,0.004437619,0.01570989,0.0036061,0.000006041899,0.0003234207,0.00002235593,0.00002931705,0.0003383337],"genre_scores_gemma":[0.9828852,0.001285346,0.01508991,0.0004472592,0.000008518657,0.00003311146,0.0002218173,0.00001125327,0.0000176087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01874196,"threshold_uncertainty_score":0.4970989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02639092059820366,"score_gpt":0.3197707732318122,"score_spread":0.2933798526336086,"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."}}