{"id":"W4394223667","doi":"10.6084/m9.figshare.20206409","title":"Additional file 4 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":"Open MIND","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; Computational biology; Biology; R package; Type 1 diabetes; Genetics; Computer science; Bioinformatics; Diabetes mellitus; Single-nucleotide polymorphism; Gene; Genotype; Gene expression; DNA methylation; Endocrinology","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":[0.00128199,0.001408024,0.001432851,0.001679302,0.0007722173,0.001801453,0.00210908,0.00186837,0.3705475],"category_scores_gemma":[0.009685947,0.0006171794,0.001369121,0.002757327,0.0003688895,0.0009349386,0.001294937,0.001266161,0.06791894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009243001,"about_ca_system_score_gemma":0.001618488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0125531,"about_ca_topic_score_gemma":0.02615507,"domain_scores_codex":[0.9993624,0.0001004601,0.00008839296,0.0002407212,0.0001034494,0.0001046727],"domain_scores_gemma":[0.9953259,0.003077019,0.0003630809,0.0004733544,0.0004867743,0.0002738304],"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":[0.0003796838,0.00007523474,0.007919123,0.003272738,0.0001993303,0.000124677,0.00006320723,0.0008568327,0.0004137846,0.0007090695,0.9820008,0.00398552],"study_design_scores_gemma":[0.004712458,0.000181665,0.04055448,0.002068387,0.0005786006,0.0005709102,0.0002629003,0.00173483,0.001592814,0.007066692,0.9405308,0.00014554],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009962604,0.00001406284,0.00005484527,0.00002353623,0.000005569432,0.000008560739,0.9995782,0.00008569059,0.0001298784],"genre_scores_gemma":[0.00201703,0.00004846837,0.0006546482,0.0001335526,0.00001390562,0.0002378169,0.9957764,0.0001541284,0.0009640718],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3705475,"threshold_uncertainty_score":0.8978376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02702274942609439,"score_gpt":0.2612556602297392,"score_spread":0.2342329108036448,"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."}}