{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005066158,0.0008435285,0.0006711397,0.0005884041,0.000443423,0.0009361022,0.001265494,0.0009450511,0.004150982],"category_scores_gemma":[0.002150541,0.0004206318,0.0009078602,0.0004409552,0.0003767294,0.0006020705,0.0008326346,0.0008426809,0.0006901668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006319221,"about_ca_system_score_gemma":0.00209552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01004617,"about_ca_topic_score_gemma":0.009919212,"domain_scores_codex":[0.9998772,0.00002891275,0.000009617827,0.0000273528,0.0000420455,0.00001475424],"domain_scores_gemma":[0.9995126,0.0002765285,0.00003948357,0.00004572567,0.00009078576,0.000034955],"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":[0.00004787698,0.00005180218,0.001512793,0.00006344031,0.00005667397,0.0001337857,0.00002371939,0.9721734,0.001389709,0.004987324,0.001776644,0.01778277],"study_design_scores_gemma":[0.000005814448,0.000006644565,0.00004137657,0.000002020822,0.000004451838,0.000006181054,0.000001929707,0.9978573,0.0001343376,0.001627457,0.0003109619,0.00000150555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0808878,0.000732925,0.8998371,0.00142739,0.0001471708,0.0002584635,0.002121649,0.005549246,0.009038194],"genre_scores_gemma":[0.6524597,0.0007087641,0.3375013,0.0003837711,0.00009818595,0.0005997572,0.002716538,0.0004784572,0.005053664],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01004617,"threshold_uncertainty_score":0.01997542,"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."}}