{"id":"W4412163776","doi":"10.1158/1557-3265.aimachine-a030","title":"Abstract A030: Advancing Pharmacogenomic Modeling: Robust Dose-Response Curve Fitting and Drug Combination Analytics in the Next-Generation PharmacoGx Framework","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"","keywords":"Pharmacogenomics; Medicine; Drug response; Drug; Analytics; Computational biology; Pharmacology; Computer science; Bioinformatics; Data science; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005249269,0.001335136,0.00103526,0.0009342355,0.0003889529,0.002931461,0.002663213,0.0008955554,0.01558191],"category_scores_gemma":[0.007589187,0.0007017055,0.002115026,0.0008676531,0.0007324946,0.002568294,0.002888909,0.002218043,0.00634811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127353,"about_ca_system_score_gemma":0.002335533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006085388,"about_ca_topic_score_gemma":0.004264798,"domain_scores_codex":[0.9983777,0.0004653461,0.0001172191,0.0003325317,0.0006191449,0.00008810095],"domain_scores_gemma":[0.99764,0.0008963607,0.0001483383,0.0006614523,0.0004671447,0.0001867272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001737613,0.000412082,0.008495473,0.001178403,0.0005643276,0.0004971428,0.0003722304,0.3006836,0.01614709,0.120175,0.2825472,0.2671897],"study_design_scores_gemma":[0.0002068694,0.0001501942,0.001249578,0.0001289984,0.00007220949,0.0001584719,0.00003208414,0.770731,0.009988206,0.07575245,0.1414405,0.00008937199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006413606,0.0007216858,0.8553066,0.001712434,0.0002086431,0.0002222096,0.01302116,0.1158184,0.006575259],"genre_scores_gemma":[0.1255447,0.001568148,0.818266,0.001576603,0.0003019454,0.0007324573,0.03231024,0.01335811,0.006341923],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01558191,"threshold_uncertainty_score":0.05212665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4284215070863578,"score_gpt":0.5580699025537492,"score_spread":0.1296483954673914,"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."}}