{"id":"W4226151130","doi":"10.1016/j.xgen.2022.100128","title":"Benchmarking challenging small variants with linked and long reads","year":2022,"lang":"en","type":"article","venue":"Cell Genomics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":209,"is_retracted":false,"has_abstract":true,"ca_institutions":"Terry Fox Research Institute; University of British Columbia","funders":"National Institute of Standards and Technology; National Institutes of Health; German Network for Bioinformatics Infrastructure; National Human Genome Research Institute; Bundesministerium für Bildung und Forschung; U.S. National Library of Medicine; Deutsche Forschungsgemeinschaft","keywords":"Benchmarking; Indel; False positive paradox; Computational biology; Computer science; Benchmark (surveying); False positives and false negatives; Genome; Biology; Data mining; Genetics; Artificial intelligence; Gene","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":[0.0129663,0.002614205,0.001331425,0.003010027,0.001868731,0.00349524,0.002361479,0.002695599,0.003360159],"category_scores_gemma":[0.04262209,0.0007651774,0.001890525,0.003085733,0.001423289,0.001710451,0.003312462,0.00213991,0.002664795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001501477,"about_ca_system_score_gemma":0.002233988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00904633,"about_ca_topic_score_gemma":0.01591587,"domain_scores_codex":[0.982238,0.005401759,0.00142544,0.005280126,0.004905668,0.0007489251],"domain_scores_gemma":[0.9779894,0.01222927,0.0009773255,0.004243249,0.003769621,0.0007911305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004595905,0.0009816736,0.1800513,0.004151191,0.004370851,0.002607603,0.002134264,0.2449728,0.1796117,0.02247917,0.08991261,0.2641309],"study_design_scores_gemma":[0.0004841998,0.001455967,0.07324292,0.0004467878,0.0007967224,0.003014727,0.0009517858,0.505786,0.2655537,0.03192096,0.1157264,0.0006198174],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5334673,0.005281107,0.3554902,0.001453993,0.001285178,0.0009805972,0.03786503,0.04784852,0.016328],"genre_scores_gemma":[0.5368395,0.0006862218,0.3200414,0.001927221,0.0002107043,0.0009543679,0.1259001,0.008677785,0.004762588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0129663,"threshold_uncertainty_score":0.06857312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007597151974323766,"score_gpt":0.1813223733136201,"score_spread":0.1737252213392963,"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."}}