{"id":"W4392371883","doi":"10.1038/s41431-024-01566-2","title":"Workshop report: the clinical application of data from multiplex assays of variant effect (MAVEs), 12 July 2023","year":2024,"lang":"en","type":"article","venue":"European Journal of Human Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research; National Heart, Lung, and Blood Institute; Australian Government; National Institutes of Health; Cancer Research UK; U.S. Department of Health and Human Services; Government of Canada; Wellcome Trust; Wellcome","keywords":"Data science; Multiplex; Computational biology; Computer science; Medical physics; Bioinformatics; Medicine; Biology","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":[0.001962144,0.0001307483,0.0002330507,0.00003941654,0.00004738074,0.00003547623,0.0008407218,0.00005178274,0.00001525317],"category_scores_gemma":[0.0002152428,0.00008859926,0.0002110381,0.00005736695,0.0001443862,0.00000460486,0.0003809001,0.000193414,0.000009704963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004363627,"about_ca_system_score_gemma":0.00009272473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003527392,"about_ca_topic_score_gemma":0.00000945052,"domain_scores_codex":[0.9979551,0.0004732902,0.0009840461,0.0002803227,0.0001924216,0.0001147841],"domain_scores_gemma":[0.998107,0.0001087485,0.000603338,0.0009671635,0.0001297632,0.00008394655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003471797,0.0004046714,0.004887476,0.0001505872,0.001410267,0.001504229,0.0002148433,0.0008234227,0.8661731,0.00004186412,0.05697933,0.06706305],"study_design_scores_gemma":[0.003956635,0.004956322,0.2020491,0.000706637,0.002385826,0.001472802,0.0003175923,0.005312569,0.08199403,0.0006010975,0.6952345,0.001012904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757991,0.01096939,0.01121215,0.0001006779,0.0005158677,0.0001850619,0.0002583149,0.000004314672,0.000955127],"genre_scores_gemma":[0.9963377,0.0006883687,0.001337228,0.0000327983,0.001097808,5.956172e-7,0.000297223,0.00003589393,0.0001724191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.784179,"threshold_uncertainty_score":0.3612973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05478707252401609,"score_gpt":0.3500564497564969,"score_spread":0.2952693772324808,"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."}}