{"id":"W2983153415","doi":"10.1016/j.ccell.2019.10.003","title":"Companion Diagnostics to Identify Biomarkers of Response to Anticancer Drugs Targeting the Proteome","year":2019,"lang":"en","type":"letter","venue":"Cancer Cell","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Drug; Proteome; Companion diagnostic; Drug response; Drug discovery; Computational biology; Cancer research; Anticancer drug; Cancer drugs; Medicine; Pharmacology; Cancer; Bioinformatics; Biology; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003036118,0.0003502734,0.0004227401,0.00006634826,0.0001163635,0.00004735496,0.0008969082,0.0004054531,0.0005494305],"category_scores_gemma":[0.0001239284,0.0002966172,0.0001540198,0.0002744384,0.00008487573,0.00004755088,0.0003036397,0.000816549,0.00008087667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004505141,"about_ca_system_score_gemma":0.0001906556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003411526,"about_ca_topic_score_gemma":0.000004589901,"domain_scores_codex":[0.9980023,0.00005402258,0.0004969597,0.000602689,0.0003902345,0.0004538118],"domain_scores_gemma":[0.9976578,0.0006080776,0.0004015714,0.001049231,0.0002072156,0.00007613523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009934426,0.00001381873,0.00008033591,0.0004136169,0.00002956863,0.000003370431,0.0002291327,0.00008277098,0.3404386,0.000003935851,0.6582911,0.0003143413],"study_design_scores_gemma":[0.0001118863,0.00001601187,0.00002126452,0.0002993297,0.00003954886,4.360571e-7,0.00006645024,0.00001592481,0.3708424,0.00007579196,0.6282383,0.0002727188],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.1515337,0.003925441,0.01942822,0.8025494,0.001065735,0.008031628,0.004031332,0.0004767002,0.008957853],"genre_scores_gemma":[0.1349788,0.004217522,0.06609692,0.73588,0.008837737,0.0165171,0.0009791763,0.0009735269,0.03151922],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.06666935,"threshold_uncertainty_score":0.9999486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388931257298402,"score_gpt":0.3096122535292603,"score_spread":0.2957229409562762,"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."}}