{"id":"W4205468246","doi":"10.21203/rs.3.rs-1175817/v1","title":"Stroke genetics informs drug discovery and risk prediction across ancestries","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hamilton Health Sciences; McGill University; Thrombosis and Atherosclerosis Research Institute; McMaster University; Population Health Research Institute","funders":"National Institute of Neurological Disorders and Stroke; Faculty of Medicine and Health, University of Sydney; Erasmus Universitair Medisch Centrum Rotterdam; Leids Universitair Medisch Centrum; Medical Research Council; Ohio State University; University of Tokyo; Agence Nationale de la Recherche; Universiteit Maastricht; Universiteit van Amsterdam; University of California, San Diego; Universiteit Leiden; University of Bristol; Statens Serum Institut; Center for Clinical and Translational Science, Ohio State University; Institut National de la Santé et de la Recherche Médicale; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Amsterdam University Medical Centers; Massachusetts General Hospital; University of Minnesota; Norges Teknisk-Naturvitenskapelige Universitet; Maastricht Universitair Medisch Centrum; University of Washington; Universitair Medisch Centrum Groningen; McKnight Foundation","keywords":"Drug discovery; Drug; Computational biology; Genetics; Computer science; Biology; Bioinformatics; Pharmacology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004623835,0.0006225131,0.001040623,0.002480559,0.0004378558,0.003979275,0.0005860854,0.001478881,0.01358208],"category_scores_gemma":[0.03088971,0.0006032976,0.0006857844,0.002943419,0.0007721647,0.002619863,0.00148575,0.001335021,0.003196813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009083343,"about_ca_system_score_gemma":0.001708071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004282157,"about_ca_topic_score_gemma":0.004301334,"domain_scores_codex":[0.9978859,0.001009213,0.0001095779,0.0004077577,0.0004892005,0.0000983842],"domain_scores_gemma":[0.9851722,0.01113705,0.0008427679,0.001771675,0.0007325675,0.0003437666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00105736,0.0002872088,0.132482,0.0008831309,0.001278412,0.001099925,0.000712161,0.05060145,0.005105623,0.2915153,0.1095132,0.4054642],"study_design_scores_gemma":[0.0001421933,0.00005968215,0.02154167,0.000125861,0.0002975055,0.0003881209,0.0001960549,0.09192961,0.002246508,0.8379964,0.0450398,0.00003664173],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2046455,0.01402148,0.5581167,0.07426414,0.002208368,0.0001119917,0.03339341,0.007056386,0.106182],"genre_scores_gemma":[0.81525,0.00698948,0.1420927,0.002389518,0.001977737,0.00007593096,0.008563196,0.0009318645,0.02172957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01358208,"threshold_uncertainty_score":0.04543656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03432576355668958,"score_gpt":0.3803038589028907,"score_spread":0.3459780953462011,"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."}}