{"id":"W3021468234","doi":"10.12688/f1000research.19444.1","title":"Exome sequencing in genetic disease: recent advances and considerations","year":2020,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Fondation Brain Canada; ALS Society of Canada; McGill University","keywords":"Exome sequencing; Disease; DNA sequencing; Exome; Scope (computer science); Computational biology; Genomics; Biology; Data science; Bioinformatics; Genome; Medicine; Genetics; Computer science; Mutation; Pathology; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001374841,0.0001875042,0.0001850064,0.00009437638,0.0000870174,0.0001067321,0.0001922151,0.0001391229,0.00008568694],"category_scores_gemma":[0.0004290801,0.0001924635,0.00005908775,0.00007208398,0.0001370005,0.000002656003,0.00103661,0.0003046654,0.00001000435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006643269,"about_ca_system_score_gemma":0.001230087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000393985,"about_ca_topic_score_gemma":0.0001202721,"domain_scores_codex":[0.9984488,0.0001368457,0.0002363057,0.0006639065,0.000213673,0.0003004775],"domain_scores_gemma":[0.9990777,0.00003154088,0.00004887385,0.0003955824,0.0001239389,0.0003224119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002265917,0.0008341699,0.1900195,0.009988364,0.0009557176,0.007462847,0.001956532,0.068279,0.5893252,0.003248165,0.04806536,0.07759912],"study_design_scores_gemma":[0.003409514,0.0007829512,0.3257833,0.0008215217,0.0002063158,0.0001460023,0.001216732,0.0111224,0.01897017,0.1253867,0.5086889,0.003465587],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8717082,0.1234195,0.0001832323,0.002615983,0.0001813679,0.0008108112,0.0004220552,0.00001877183,0.0006400516],"genre_scores_gemma":[0.9370872,0.06060738,0.0009317615,0.0003146477,0.0002616965,0.0001542677,0.0004526423,0.00003767763,0.0001527053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5703551,"threshold_uncertainty_score":0.7848433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05335927925827001,"score_gpt":0.334326748086525,"score_spread":0.280967468828255,"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."}}