{"id":"W1978851402","doi":"10.3389/fgene.2014.00325","title":"DaVIE: Database for the Visualization and Integration of Epigenetic data","year":2014,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Child and Family Research Institute","funders":"Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Visualization; Computer science; Database; Epigenetics; Data visualization; World Wide Web; Biology; Data mining; 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":[0.006434739,0.002884874,0.003265993,0.007122703,0.001428884,0.008033883,0.007097837,0.002710088,0.05402001],"category_scores_gemma":[0.02159468,0.00189222,0.001878975,0.006235699,0.0009258144,0.008468715,0.007931583,0.004706292,0.04045911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001576355,"about_ca_system_score_gemma":0.003868958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004965,"about_ca_topic_score_gemma":0.005744086,"domain_scores_codex":[0.9950906,0.000906407,0.000881289,0.001174151,0.001543946,0.0004036933],"domain_scores_gemma":[0.9899192,0.003300474,0.0009618397,0.002564585,0.002163457,0.001090416],"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":[0.001604071,0.0001494861,0.003103245,0.004360304,0.0005307554,0.0008138895,0.0009645609,0.002466923,0.01372682,0.02396509,0.8230537,0.1252613],"study_design_scores_gemma":[0.0003609279,0.00007522172,0.002414946,0.0004736134,0.0001458741,0.0007611505,0.0002641915,0.007110444,0.01459971,0.02642022,0.9470906,0.0002830744],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.00384905,0.005317212,0.2629547,0.001829686,0.000961803,0.0008835369,0.4233674,0.2752212,0.02561537],"genre_scores_gemma":[0.02447725,0.003824973,0.3236988,0.002368907,0.000260507,0.002726787,0.5914401,0.03693758,0.01426522],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05402001,"threshold_uncertainty_score":0.1807149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03316461524196006,"score_gpt":0.2996000274020513,"score_spread":0.2664354121600913,"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."}}