{"id":"W2528021625","doi":"10.1186/s13040-017-0129-5","title":"Variant Set Enrichment: an R package to identify disease-associated functional genomic regions","year":2017,"lang":"en","type":"article","venue":"BioData Mining","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Ontario Institute for Cancer Research; University of Toronto; Princess Margaret Cancer Centre","funders":"U.S. National Library of Medicine; National Institute of Environmental Health Sciences; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Cancer Institute; National Institutes of Health; Prostate Cancer Canada; Natural Sciences and Engineering Research Council of Canada; Princess Margaret Cancer Foundation","keywords":"Computational biology; Disease; Set (abstract data type); Genome; R package; Human genome; Computer science; Biology; Genetics; Gene; Medicine","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":[0.0001635781,0.0001580715,0.0001142665,0.00004403819,0.0006131394,0.000258054,0.0005121229,0.0000825309,0.00005203029],"category_scores_gemma":[0.0002443164,0.0001599778,0.000082007,0.00002958017,0.00005396561,0.00001593428,0.0004401143,0.00004326829,0.00006035819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002456577,"about_ca_system_score_gemma":0.0001323388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004115216,"about_ca_topic_score_gemma":0.00007308572,"domain_scores_codex":[0.9988866,0.00003978664,0.0001651996,0.0005094143,0.0001178798,0.0002811002],"domain_scores_gemma":[0.9983409,0.000007023969,0.0001344814,0.001103518,0.0000593586,0.0003547138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005127924,0.000557189,0.09996618,0.00003315358,0.0004852827,0.0003903622,0.0003080857,0.00007561444,0.78363,0.0007592381,0.1104134,0.002868678],"study_design_scores_gemma":[0.0006449974,0.000144402,0.9551973,0.00002633648,0.00009620297,0.00001689805,0.000185677,0.00007355188,0.001384488,0.0001164647,0.04169673,0.0004169175],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961224,0.000262652,0.0006533072,0.000359256,0.0004150873,0.0001510797,0.001694254,0.00001727752,0.000324675],"genre_scores_gemma":[0.9934433,0.00005694859,0.000329553,0.0004419348,0.0003518689,0.00002413854,0.004634201,0.00002566913,0.0006923586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8552312,"threshold_uncertainty_score":0.6523705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05028613776478923,"score_gpt":0.3121266194218884,"score_spread":0.2618404816570991,"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."}}