{"id":"W2587112482","doi":"10.1093/bioinformatics/btw826","title":"VEXOR: an integrative environment for prioritization of functional variants in fine-mapping analysis","year":2017,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier universitaire de Québec","funders":"National Human Genome Research Institute; Health Canada; National Cancer Institute; National Institutes of Health; Government of Canada; Fondation du cancer du sein du Québec; Compute Canada; Canadian Institutes of Health Research; Genome Canada","keywords":"Prioritization; Computer science; Genome-wide association study; Interface (matter); Identification (biology); Genetic variants; Association (psychology); User interface; Computational biology; Data science; Data mining; Biology; Single-nucleotide polymorphism; Genetics","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":[0.006723171,0.002427819,0.001842306,0.003566506,0.0008103471,0.003164294,0.003716754,0.001513381,0.04252569],"category_scores_gemma":[0.01210953,0.001408065,0.002501983,0.002238625,0.0008020931,0.003045185,0.006129136,0.001945121,0.01630786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007406771,"about_ca_system_score_gemma":0.001308919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00248142,"about_ca_topic_score_gemma":0.003091656,"domain_scores_codex":[0.9978939,0.0006383216,0.000201998,0.0006092488,0.000475582,0.0001809544],"domain_scores_gemma":[0.9926111,0.00560078,0.0003794158,0.0006615684,0.0003386178,0.000408551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.007034477,0.0005402797,0.01538787,0.006774645,0.001582593,0.003196928,0.002761257,0.01396165,0.03918437,0.03017121,0.52149,0.3579148],"study_design_scores_gemma":[0.003782356,0.0009005397,0.02983261,0.002054743,0.000879064,0.003847178,0.0008171971,0.1836298,0.04876888,0.1044448,0.61992,0.001122914],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.01155025,0.001601575,0.4225962,0.001013767,0.0003886998,0.0007099136,0.04239472,0.5094715,0.01027336],"genre_scores_gemma":[0.09183737,0.001880484,0.7345407,0.001531946,0.0002755488,0.002306791,0.07365444,0.0828663,0.01110646],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.04252569,"threshold_uncertainty_score":0.1422626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02291473297309204,"score_gpt":0.2727190200054641,"score_spread":0.2498042870323721,"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."}}