{"id":"W2799455659","doi":"10.1177/2472555218773045","title":"Application of Integrated Drug Screening/Kinome Analysis to Identify Inhibitors of Gemcitabine-Resistant Pancreatic Cancer Cell Growth","year":2018,"lang":"en","type":"article","venue":"SLAS DISCOVERY","topic":"Pancreatic and Hepatic Oncology Research","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Institute of Mental Health; Triangle Comparative and Evolutionary Medicine Center, Duke University; National Institutes of Health","keywords":"Kinome; Cyclin-dependent kinase; Kinase; Gemcitabine; Biology; CDK inhibitor; Pancreatic cancer; Cell growth; Cancer research; Growth inhibition; Cyclin-dependent kinase 2; Cell cycle; Cell biology; Cancer; Cell; Biochemistry; Protein kinase A; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004537027,0.0001773243,0.0008629283,0.000417799,0.00005604296,0.00001528193,0.0001850176,0.00009491867,0.0002867281],"category_scores_gemma":[0.0001741152,0.000139788,0.0002242605,0.001549939,0.0003158936,0.0001667101,0.00009119712,0.000128512,0.00002257536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001839253,"about_ca_system_score_gemma":0.0003468669,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01790881,"about_ca_topic_score_gemma":0.001913264,"domain_scores_codex":[0.9981376,0.00008977037,0.000621355,0.0003575763,0.0004944301,0.0002992648],"domain_scores_gemma":[0.9983838,0.0001565432,0.0002691627,0.000479427,0.0005520418,0.0001589914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000999699,0.0003027217,0.8597263,0.0002774071,0.001257588,0.00000441408,0.0006947141,0.00001020022,0.1347174,0.0005007357,0.00107112,0.0004376731],"study_design_scores_gemma":[0.001243518,0.0005867028,0.8881187,0.0002166768,0.00345835,0.000001221691,0.001146804,0.001623727,0.1029694,0.0001881235,0.0002603373,0.000186456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97949,0.0004909543,0.01626462,0.0002264427,0.00005826747,0.0005921451,0.0001423969,0.00002085749,0.002714287],"genre_scores_gemma":[0.9957223,0.0001146158,0.001451687,0.00008408479,0.0001416638,0.000106612,0.0000892341,0.00002141883,0.002268382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03174805,"threshold_uncertainty_score":0.988631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01672909287430834,"score_gpt":0.3394272727758373,"score_spread":0.3226981799015289,"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."}}