{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008040396,0.00353864,0.002920498,0.003433142,0.001295482,0.003452675,0.005397295,0.001682863,0.1120858],"category_scores_gemma":[0.0239225,0.003296023,0.002928246,0.003305533,0.001110196,0.001911678,0.004128762,0.004366717,0.06844548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009379931,"about_ca_system_score_gemma":0.003741878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003302254,"about_ca_topic_score_gemma":0.006139494,"domain_scores_codex":[0.9960978,0.001287332,0.0004091805,0.0009922459,0.00087625,0.0003372156],"domain_scores_gemma":[0.992774,0.004420954,0.0006874871,0.001001794,0.0007448198,0.0003710011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001981969,0.0002264239,0.009502209,0.004410639,0.001785029,0.001452345,0.0007973512,0.01404781,0.01633139,0.01918289,0.8139679,0.1163142],"study_design_scores_gemma":[0.002442728,0.0003945697,0.0129283,0.001204203,0.0009317163,0.002437522,0.0002021798,0.09501486,0.02284962,0.06925678,0.7916056,0.0007319733],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00507878,0.001006024,0.4613841,0.0007028215,0.001015059,0.001079275,0.1579192,0.3647168,0.00709791],"genre_scores_gemma":[0.02084266,0.0008259353,0.6840246,0.001473791,0.0002817756,0.005080856,0.1705238,0.105618,0.01132858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1120858,"threshold_uncertainty_score":0.3749643,"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."}}