{"id":"W2795797559","doi":"10.3791/57266","title":"Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease","year":2018,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; McMaster University; Toronto Western Hospital; Occupational Cancer Research Centre; Health Sciences Centre; Agricultural Research Institute of Ontario; Sunnybrook Health Science Centre; St Joseph's Health Care; Western University; University of Toronto; University of Ottawa; Parkwood Institute; Queen's University","funders":"Alzheimer Society; ALS Society of Canada","keywords":"DNA sequencing; Computational biology; Genomics; Workflow; Exome sequencing; Identification (biology); Genome; Whole genome sequencing; Biology; Genetics; Computer science; Bioinformatics; Mutation; 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":[0.0001549238,0.000106538,0.0001628807,0.00008013367,0.00006811663,0.00002600587,0.00009295816,0.00004125402,0.00002631447],"category_scores_gemma":[0.0001303524,0.0000899001,0.00006664314,0.00004891006,0.0001514607,0.0000122614,0.0000625381,0.00002432185,0.000002689027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002283716,"about_ca_system_score_gemma":0.0002875915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003691039,"about_ca_topic_score_gemma":0.000001363367,"domain_scores_codex":[0.9990669,0.0000406098,0.0004766599,0.000103408,0.0001912187,0.0001211673],"domain_scores_gemma":[0.9990308,0.000004351265,0.0002839127,0.0001134012,0.0003561132,0.0002114242],"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":[0.0003267717,0.0000662891,0.001522725,0.00001725833,0.00003982501,0.000009156959,0.0001917946,0.00007259927,0.9955851,0.00001240888,0.0007387288,0.001417382],"study_design_scores_gemma":[0.001639097,0.0007376689,0.002365544,0.0000618468,0.00007244967,0.00007764151,0.0001815509,0.008625253,0.984697,0.00003109572,0.001336156,0.000174727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907267,0.001592229,0.007255935,0.00001627131,0.0002382343,0.0001173183,0.00002689636,0.000001440319,0.00002494105],"genre_scores_gemma":[0.9876479,0.0001511085,0.01156986,0.0002696133,0.000307608,0.00000304009,0.0000199799,0.000007643782,0.00002327992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01088809,"threshold_uncertainty_score":0.3666019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05517437892079485,"score_gpt":0.395536175396312,"score_spread":0.3403617964755171,"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."}}