{"id":"W3034581902","doi":"10.1038/s41587-020-0538-8","title":"A robust benchmark for detection of germline large deletions and insertions","year":2020,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":483,"is_retracted":false,"has_abstract":false,"ca_institutions":"Genome British Columbia; Centre Hospitalier Universitaire Sainte-Justine; Ontario Institute for Cancer Research","funders":"National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; National Institute of Standards and Technology; U.S. National Library of Medicine; U.S. Food and Drug Administration; U.S. Department of Health and Human Services; National Institutes of Health; U.S. Department of Commerce","keywords":"Benchmark (surveying); False positive paradox; Computational biology; Genome; Germline; Biology; Human genome; Set (abstract data type); Genetics; Computer science; Structural variation; Sequence (biology); 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":[0.01106296,0.001786313,0.001707881,0.004413093,0.001615982,0.004510124,0.002671833,0.005455284,0.00451673],"category_scores_gemma":[0.03071237,0.0007901046,0.000914263,0.003189926,0.001233933,0.002510721,0.002831173,0.00188636,0.003848095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001494256,"about_ca_system_score_gemma":0.001349097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001335511,"about_ca_topic_score_gemma":0.001787764,"domain_scores_codex":[0.9854526,0.003124928,0.001098463,0.003509397,0.00589467,0.0009199668],"domain_scores_gemma":[0.9816566,0.006857649,0.001959488,0.003725315,0.004759907,0.001041003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002373028,0.0006177836,0.04849107,0.001110939,0.0006162766,0.0005825293,0.0002799784,0.0343411,0.6912878,0.01804955,0.01831709,0.183933],"study_design_scores_gemma":[0.0001184197,0.001573283,0.03758682,0.0002445305,0.0002924201,0.001956474,0.0002531022,0.1835971,0.7036338,0.02378785,0.04672262,0.0002335562],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2290463,0.004800818,0.6983612,0.001497903,0.0005439445,0.0005715252,0.01651756,0.02821244,0.0204484],"genre_scores_gemma":[0.5673155,0.0008621992,0.3905194,0.00113846,0.0001882628,0.001093995,0.03073875,0.002223364,0.005920084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01106296,"threshold_uncertainty_score":0.05850726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0101585750646057,"score_gpt":0.2244963837058896,"score_spread":0.2143378086412839,"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."}}