{"id":"W4394355853","doi":"10.6084/m9.figshare.20206403","title":"Additional file 2 of 3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Diabetes and associated disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Epigenomics; SNP; Type 1 diabetes; Computational biology; Type 2 diabetes; Biology; Genetics; Bioinformatics; Diabetes mellitus; Single-nucleotide polymorphism; Gene; DNA methylation; Endocrinology; Gene expression; Genotype","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001528675,0.001511873,0.001533528,0.001993196,0.000889026,0.00202431,0.002292618,0.0019326,0.4619293],"category_scores_gemma":[0.0126343,0.0007798976,0.001530129,0.003241081,0.0003794862,0.0009980358,0.001408845,0.001314849,0.09931894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008684782,"about_ca_system_score_gemma":0.001824389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01067209,"about_ca_topic_score_gemma":0.02049622,"domain_scores_codex":[0.9992104,0.0001427281,0.0001071469,0.0003058227,0.000121716,0.0001121791],"domain_scores_gemma":[0.9932724,0.004778595,0.0004051818,0.0006523248,0.0005706981,0.0003208172],"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":[0.000349889,0.00006545348,0.005077248,0.002759941,0.0001519485,0.0001177825,0.000069489,0.0007964598,0.0003719523,0.0008497484,0.9853641,0.004025836],"study_design_scores_gemma":[0.004578872,0.0001435152,0.0250153,0.001682567,0.0005317085,0.0005758238,0.0001963082,0.00139012,0.00142573,0.009085224,0.9552422,0.0001325534],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008767017,0.00001471359,0.00009409619,0.00002565435,0.000006513649,0.00001192822,0.9994467,0.0001282217,0.0001844673],"genre_scores_gemma":[0.001623835,0.00004721893,0.0009392039,0.0001431312,0.0000138868,0.000321846,0.9956509,0.0002658806,0.0009941038],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4619293,"threshold_uncertainty_score":0.7674925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02543144539668855,"score_gpt":0.2352235729148019,"score_spread":0.2097921275181134,"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."}}