{"id":"W4367679348","doi":"10.1101/2023.04.27.538531","title":"WEGS: a cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"Alliance de recherche numérique du Canada; Fonds de Recherche du Québec - Santé; Genome Canada; McGill University","keywords":"Exome sequencing; Genotyping; Exome; Whole genome sequencing; Genome; Biology; Computational biology; Locus (genetics); Imputation (statistics); DNA sequencing; Population; 1000 Genomes Project; Genetics; Computer science; Genotype; Gene; Missing data; Single-nucleotide polymorphism; Machine learning; Medicine; Mutation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002500737,0.001126618,0.0007654217,0.001890394,0.0004622408,0.001117497,0.001114137,0.00121489,0.005241211],"category_scores_gemma":[0.003308709,0.000899734,0.0007894822,0.001635864,0.0005283351,0.001093761,0.001726322,0.0009248895,0.001539514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004376472,"about_ca_system_score_gemma":0.0007449389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001085264,"about_ca_topic_score_gemma":0.002657829,"domain_scores_codex":[0.9978161,0.0006879345,0.0001447321,0.0005247718,0.0006994025,0.0001270331],"domain_scores_gemma":[0.9981188,0.000809613,0.0002600032,0.0003833805,0.0002911543,0.0001370168],"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":[0.0009890107,0.0002614106,0.01280614,0.0005175783,0.001031039,0.0004844563,0.0001514959,0.02848676,0.7012355,0.006134205,0.01594947,0.231953],"study_design_scores_gemma":[0.0003798778,0.0007669427,0.02640925,0.000114711,0.0004506586,0.001913587,0.0001381526,0.4427612,0.4524533,0.01592358,0.05832075,0.0003679066],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08933934,0.000906216,0.8946545,0.0004423126,0.0002728543,0.0003131964,0.002570269,0.00947803,0.002023286],"genre_scores_gemma":[0.1646615,0.0003206862,0.8277867,0.0003994675,0.00007472205,0.0005566256,0.002820407,0.0007576023,0.002622249],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005241211,"threshold_uncertainty_score":0.0175336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02722995132018869,"score_gpt":0.2798876819224456,"score_spread":0.2526577306022569,"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."}}