{"id":"W2979095428","doi":"10.1093/bioinformatics/btz716","title":"VikNGS: a C++ variant integration kit for next generation sequencing association analysis","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; SickKids Foundation; Hospital for Sick Children","funders":"Wellcome Trust; Genome Canada","keywords":"Computer science; Spurious relationship; Data mining; Covariate; Software; R package; Graphical user interface; Genetic association; Visualization; Association test; Sample size determination; Computational biology; Genotype; Statistics; Biology; Machine learning; Operating system; Genetics; Single-nucleotide polymorphism; Computational science; Mathematics","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":[0.0007342593,0.0001219907,0.0002125301,0.0001038737,0.00009126707,0.00005940471,0.0001027929,0.0002523768,0.00002928206],"category_scores_gemma":[0.0004744799,0.0001135517,0.0001826054,0.0002136066,0.000007939086,0.00001727488,0.00003275879,0.00005514479,0.00004734969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001689025,"about_ca_system_score_gemma":0.00009785962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003352275,"about_ca_topic_score_gemma":0.0001166738,"domain_scores_codex":[0.9989924,0.00004956543,0.0004411219,0.0001645883,0.0001202969,0.0002319962],"domain_scores_gemma":[0.999046,0.000047226,0.0003821949,0.000249614,0.0002309638,0.0000440224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009743238,0.0001357746,0.09752575,0.0001587819,0.003854076,5.077523e-7,0.002830352,0.04272722,0.7745181,0.00335514,0.04847061,0.02632622],"study_design_scores_gemma":[0.0006908806,0.0003150046,0.008208641,0.000007447077,0.0004406993,0.000003055854,0.0009460221,0.9658597,0.007585815,0.0001516386,0.01545985,0.0003313004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6399962,0.00005187369,0.3573308,0.0002488425,0.0003260167,0.0004548234,0.00007348011,0.00001777513,0.001500216],"genre_scores_gemma":[0.9358997,0.00006969277,0.05924621,0.000726694,0.0002607457,0.00005213998,0.002479161,0.0000116304,0.001253983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9231324,"threshold_uncertainty_score":0.4630502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03261885538734776,"score_gpt":0.2630504061342485,"score_spread":0.2304315507469007,"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."}}