{"id":"W4319748040","doi":"10.1186/s12859-023-05141-2","title":"Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data","year":2023,"lang":"en","type":"review","venue":"BMC Bioinformatics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"National Institute of General Medical Sciences; Intellectual and Developmental Disabilities Research Center; National Institutes of Health; Eunice Kennedy Shriver National Institute of Child Health and Human Development; University of Pennsylvania","keywords":"Interpretability; Leverage (statistics); Computer science; Machine learning; Artificial intelligence; Annotation; Labeled data; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003509016,0.0004215399,0.0009001091,0.00008709655,0.00007701117,0.00004313398,0.0007356993,0.0003449004,0.000003161639],"category_scores_gemma":[0.0001083596,0.0003096014,0.0001406084,0.0002090112,0.0001088104,0.00001922093,0.00112723,0.0001647826,0.000005859355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002766985,"about_ca_system_score_gemma":0.0009234112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005365136,"about_ca_topic_score_gemma":0.00003563095,"domain_scores_codex":[0.99819,0.00006301152,0.0008112905,0.0004473059,0.0001813718,0.0003069559],"domain_scores_gemma":[0.9980062,0.00004206433,0.0003669679,0.001276164,0.000118738,0.0001898377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001847844,0.0005552405,0.0009067646,0.1184387,0.005191758,0.00001212015,0.001383049,0.1316494,0.00008743723,0.00006651005,0.009959547,0.7299017],"study_design_scores_gemma":[0.0005890686,0.0003881995,0.00002096787,0.002112532,0.0008422632,0.00002308957,0.00006833555,0.9748746,0.00000779739,0.000002624254,0.02058026,0.0004902489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.006812425,0.8182771,0.1633949,0.000006417074,0.0004174543,0.00345114,0.007311778,0.0000555445,0.0002732128],"genre_scores_gemma":[0.0009058538,0.9651815,0.02854234,0.00004928879,0.0001790239,0.00006426354,0.004917692,0.00006415954,0.00009584951],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8432252,"threshold_uncertainty_score":0.9999356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06189355387424046,"score_gpt":0.3003453905127546,"score_spread":0.2384518366385141,"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."}}