{"id":"W2162080268","doi":"10.1186/2050-6511-15-66","title":"Exploiting high-throughput cell line drug screening studies to identify candidate therapeutic agents in head and neck cancer","year":2014,"lang":"en","type":"article","venue":"BMC Pharmacology and Toxicology","topic":"Head and Neck Cancer Studies","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research; Université de Montréal; Western University","funders":"Government of Ontario; Ontario Institute for Cancer Research","keywords":"Head and neck cancer; Drug; Cancer; Cancer cell lines; Head and neck; Throughput; Medicine; Drug candidate; Computational biology; Oncology; Computer science; Pharmacology; Internal medicine; Cancer cell; Biology; Surgery","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":[0.001714859,0.0006739278,0.001118865,0.0008691595,0.0003174707,0.0008957658,0.0005235344,0.0005023025,0.001247771],"category_scores_gemma":[0.0009180462,0.0002579395,0.0009367338,0.000810665,0.0003664671,0.0005444395,0.0004154847,0.001040451,0.0004599745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008839525,"about_ca_system_score_gemma":0.0005932259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002011531,"about_ca_topic_score_gemma":0.003980443,"domain_scores_codex":[0.9992064,0.0002335328,0.00007374981,0.0001468835,0.0002680791,0.00007135014],"domain_scores_gemma":[0.9986081,0.0006642891,0.0002017135,0.0002369456,0.0002249936,0.00006410959],"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":[0.0007508787,0.001055269,0.01355366,0.0004296639,0.0004318543,0.00009144189,0.00008050503,0.004571188,0.9477801,0.00015851,0.0006893466,0.03040772],"study_design_scores_gemma":[0.0001088722,0.006554581,0.07394227,0.00002204985,0.0005793609,0.0005121665,0.00008782264,0.01076748,0.9021967,0.0002855623,0.004888365,0.00005483006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9526473,0.008606227,0.03170675,0.0003454793,0.0000733501,0.0006786406,0.003543353,0.0005438146,0.001855158],"genre_scores_gemma":[0.9679496,0.004252246,0.02118291,0.0002906022,0.00003793922,0.0004808031,0.004519084,0.00007979192,0.00120707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002011531,"threshold_uncertainty_score":0.009069145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1067513436401301,"score_gpt":0.4448636873711024,"score_spread":0.3381123437309723,"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."}}