{"id":"W4405307701","doi":"10.1093/bib/bbae645","title":"Detection of germline CNVs from gene panel data: benchmarking the state of the art","year":2024,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"Departament de Salut, Generalitat de Catalunya; Instituto de Salud Carlos III; Centro de Investigación Biomédica en Red de Cáncer; Medizinische Universität Innsbruck; Agència de Gestió d'Ajuts Universitaris i de Recerca; Fundació la Marató de TV3; Generalitat de Catalunya; Universität Innsbruck; Centres de Recerca de Catalunya","keywords":"Benchmarking; Copy-number variation; Computer science; Germline; Context (archaeology); Computational biology; Gene; Biology; Genetics; Genome","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":[0.0331483,0.003532732,0.002172007,0.01092249,0.002146916,0.005321362,0.005726608,0.002757658,0.004412046],"category_scores_gemma":[0.0868563,0.001303013,0.003942604,0.006808499,0.001422963,0.004364025,0.005420611,0.002212364,0.002987348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001753638,"about_ca_system_score_gemma":0.003203713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01231793,"about_ca_topic_score_gemma":0.0169345,"domain_scores_codex":[0.9683378,0.01113119,0.003088325,0.008370304,0.007768592,0.001303851],"domain_scores_gemma":[0.9528465,0.0309963,0.001385484,0.008312506,0.005417708,0.001041565],"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":[0.003665748,0.0006604768,0.1138447,0.01017867,0.004630185,0.001354611,0.002692328,0.1117949,0.04528493,0.00760952,0.1193264,0.5789576],"study_design_scores_gemma":[0.00113792,0.002146899,0.08747984,0.004300225,0.003435521,0.003913682,0.001818227,0.4483932,0.143201,0.0300374,0.2724977,0.001638285],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4703289,0.03602969,0.2447557,0.004003371,0.001956824,0.001128326,0.08295344,0.1393539,0.01948985],"genre_scores_gemma":[0.3433203,0.005837248,0.4157299,0.001742649,0.0002558061,0.001237058,0.2089533,0.02010197,0.002821779],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0331483,"threshold_uncertainty_score":0.1753071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01610827388583754,"score_gpt":0.222651089033032,"score_spread":0.2065428151471944,"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."}}