{"id":"W4379207264","doi":"10.1186/s13148-023-01513-w","title":"Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data","year":2023,"lang":"en","type":"article","venue":"Clinical Epigenetics","topic":"Systemic Sclerosis and Related Diseases","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; McGill University Health Centre; Jewish General Hospital; McGill University","funders":"Lady Davis Institute for Medical Research; Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; Genome Canada","keywords":"Computational biology; Human genetics; Genome; Whole genome sequencing; Biology; Bioinformatics; Genetics; Gene","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":[0.001588484,0.0007883441,0.0006708876,0.00161882,0.0005757122,0.001419362,0.0008443953,0.0005594688,0.001566024],"category_scores_gemma":[0.003658367,0.0004160877,0.001566631,0.0009577278,0.0003913726,0.0005883003,0.000754387,0.0008559773,0.0003465328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007486812,"about_ca_system_score_gemma":0.001935936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005024858,"about_ca_topic_score_gemma":0.007612178,"domain_scores_codex":[0.9996495,0.000109135,0.00001907144,0.0000989844,0.0001013505,0.00002195607],"domain_scores_gemma":[0.9989344,0.0007375935,0.00009261082,0.00007213618,0.0001124521,0.00005071824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004567033,0.0002720467,0.03268057,0.0005596148,0.001351939,0.0003977703,0.0003158563,0.7440754,0.02676419,0.02158949,0.004950802,0.1665856],"study_design_scores_gemma":[0.00001137577,0.00001801571,0.001238617,0.000008521626,0.00003783637,0.00003624209,0.00001821072,0.9930859,0.001064459,0.003452731,0.001019192,0.000008882089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1017914,0.0003059999,0.8913322,0.0003370461,0.00006097853,0.0001397336,0.001327117,0.003310736,0.001394846],"genre_scores_gemma":[0.2621991,0.0002627774,0.7331004,0.0001664203,0.00005044149,0.0004024946,0.002431353,0.0003441269,0.001042941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005024858,"threshold_uncertainty_score":0.009991229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2757785640411662,"score_gpt":0.4335411100768395,"score_spread":0.1577625460356733,"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."}}