{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002140356,0.001016722,0.0008933942,0.001954338,0.001007286,0.001663116,0.0009141163,0.0007566232,0.007571008],"category_scores_gemma":[0.002267535,0.0006799424,0.001179202,0.00121579,0.0003296853,0.0007762438,0.001196174,0.001666306,0.005035141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123943,"about_ca_system_score_gemma":0.003152214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00417953,"about_ca_topic_score_gemma":0.006538107,"domain_scores_codex":[0.9992053,0.0001140594,0.00005944002,0.0002571096,0.0002794385,0.00008467844],"domain_scores_gemma":[0.9992674,0.0002229404,0.00007458237,0.00009915968,0.0002322433,0.0001035993],"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":[0.001302967,0.0003227927,0.01810003,0.001668224,0.0004421404,0.001600521,0.0009272253,0.02453942,0.5158428,0.01820596,0.09669186,0.3203561],"study_design_scores_gemma":[0.0003745817,0.0006117151,0.04094901,0.0003811161,0.0004060583,0.002439524,0.0004756968,0.2827792,0.3173559,0.03758496,0.3162347,0.0004073825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03700631,0.00127899,0.8941396,0.001897351,0.0003414866,0.001216513,0.02659757,0.0299343,0.007587924],"genre_scores_gemma":[0.08163027,0.001217703,0.8702871,0.001028183,0.0001121709,0.002097479,0.03454313,0.002339078,0.006744907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007571008,"threshold_uncertainty_score":0.02532756,"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."}}