{"id":"W3009375540","doi":"10.1161/circresaha.119.316404","title":"A Computational Pipeline to Predict Cardiotoxicity","year":2020,"lang":"en","type":"article","venue":"Circulation Research","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"hERG; Proarrhythmia; Cardiotoxicity; QT interval; Drug discovery; Drug; Computational biology; Computer science; Torsades de pointes; Cardiac electrophysiology; Drug development; Pharmacology; Medicine; Bioinformatics; Biology; Internal medicine; Potassium channel; Electrophysiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003411883,0.00006253683,0.0001768926,0.0001093882,0.0001266647,0.00001454319,0.00004677361,0.00006494689,0.0001656036],"category_scores_gemma":[0.000554256,0.00006038884,0.00008853826,0.0006102793,0.00006506676,0.00003795748,0.00004874665,0.0003256789,0.000492506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006390305,"about_ca_system_score_gemma":0.0001719655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000101057,"about_ca_topic_score_gemma":4.81738e-7,"domain_scores_codex":[0.9986946,0.0001411554,0.0001441517,0.0002614479,0.0005201,0.0002385546],"domain_scores_gemma":[0.999051,0.000128948,0.00001287941,0.0001340684,0.0003820394,0.0002910977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001752269,0.0001626187,0.04931984,0.0002217994,0.0002330307,0.0005376312,0.001695421,0.7017642,0.08664239,0.00334539,0.1417668,0.01255862],"study_design_scores_gemma":[0.001771997,0.0006122302,0.7250283,0.00003841753,0.00003397403,0.0001422257,0.00007767395,0.2591888,0.001533498,0.001160388,0.01021867,0.0001938319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9492818,0.00007725632,0.01716018,0.02825178,0.00004267573,0.000662737,0.00001204532,0.00008484428,0.004426699],"genre_scores_gemma":[0.9960809,0.000003976666,0.0003690758,0.002564032,0.0007513313,0.00002777205,0.00007168057,0.0000113299,0.0001199128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6757085,"threshold_uncertainty_score":0.6330333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07566225335123618,"score_gpt":0.3786414405452449,"score_spread":0.3029791871940087,"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."}}