{"id":"W2330399655","doi":"10.1101/026534","title":"Using cell line and patient samples to improve predictions of patient drug response","year":2015,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; SickKids Foundation; University of Toronto","funders":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; Sick Kids Foundation; Canadian Cancer Society Research Institute; Cancer Research Society","keywords":"Predictive modelling; Patient data; Clinical trial; Profiling (computer programming); Drug response; Personalized medicine; Precision medicine","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":[0.002647336,0.001235642,0.001196739,0.001085068,0.0001758287,0.001342614,0.000624952,0.0008113211,0.001834978],"category_scores_gemma":[0.005414735,0.0002326533,0.0007743199,0.001082813,0.0003479102,0.0006515222,0.0006014145,0.001429182,0.0008598255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005081687,"about_ca_system_score_gemma":0.0005801086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002614582,"about_ca_topic_score_gemma":0.002120136,"domain_scores_codex":[0.9991524,0.0003393557,0.00006224548,0.0002538532,0.000137804,0.00005432239],"domain_scores_gemma":[0.9963082,0.002196266,0.0003324173,0.0005577329,0.0004475063,0.0001578486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003665969,0.001408649,0.3805342,0.0005698404,0.001417495,0.000794293,0.0003435717,0.3253,0.09903398,0.001506294,0.00854954,0.1768762],"study_design_scores_gemma":[0.0002122402,0.000887625,0.07255011,0.00006668911,0.0005554006,0.0006292769,0.0002165478,0.8215711,0.09302714,0.003243959,0.006946243,0.00009361809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8603213,0.002393978,0.1206589,0.0008333513,0.0001508982,0.0002547934,0.01077619,0.001992885,0.002617915],"genre_scores_gemma":[0.9644087,0.0003467419,0.02661381,0.0001812333,0.00004317876,0.00009634984,0.007770365,0.00006923418,0.0004703127],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002647336,"threshold_uncertainty_score":0.01400065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01233243657610324,"score_gpt":0.2410880184323593,"score_spread":0.2287555818562561,"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."}}