{"id":"W4405434042","doi":"10.48550/arxiv.2412.10262","title":"VEPerform: a web resource for evaluating the performance of variant effect predictors.","year":2024,"lang":"en","type":"preprint","venue":"PubMed","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"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 Institutes of Health","keywords":"Recall; Missense mutation; Precision and recall; Computer science; Pathogenicity; Machine learning; Artificial intelligence; Web resource; Psychology; Cognitive psychology; Genetics; Gene; Biology; Mutation","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":[0.00724531,0.002981252,0.002226624,0.01023544,0.0006131629,0.00244345,0.002973981,0.00203945,0.03883922],"category_scores_gemma":[0.04321981,0.001096349,0.001744903,0.006735098,0.0004217447,0.002438047,0.002509671,0.001547328,0.02947354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005513844,"about_ca_system_score_gemma":0.002145183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003132813,"about_ca_topic_score_gemma":0.005493225,"domain_scores_codex":[0.9956671,0.001307786,0.0008011109,0.0006013304,0.001416264,0.0002064953],"domain_scores_gemma":[0.9545529,0.03666371,0.001984868,0.003078243,0.00296242,0.0007578782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002056195,0.0004548815,0.03052663,0.007691195,0.002285938,0.0008429991,0.0002415467,0.007236566,0.005737792,0.003421874,0.8013871,0.1381172],"study_design_scores_gemma":[0.003505407,0.001364737,0.06731645,0.002641192,0.001964732,0.004511746,0.0003926392,0.1161985,0.03300425,0.04674067,0.7213746,0.0009850475],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01547995,0.004521944,0.05352665,0.0006876874,0.0004071112,0.0004619062,0.6889213,0.2283436,0.007649769],"genre_scores_gemma":[0.06018775,0.002457051,0.1093464,0.0009595006,0.0004055548,0.001791807,0.7952405,0.02473788,0.004873659],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03883922,"threshold_uncertainty_score":0.1299301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01586941851773541,"score_gpt":0.2551797469038433,"score_spread":0.2393103283861079,"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."}}