{"id":"W4313289828","doi":"10.21203/rs.3.rs-2313717/v1","title":"Novel Insights into Systemic Sclerosis using a Sensitive Computational Method to Analyze Whole-Genome Bisulfite Sequencing Data","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Systemic Sclerosis and Related Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; McGill University Health Centre; Jewish General Hospital; McGill University","funders":"","keywords":"Differentially methylated regions; DNA methylation; CpG site; Bisulfite sequencing; Biology; Illumina Methylation Assay; Genetics; Computational biology; Methylation; Epigenetics; Genome; Gene; Bonferroni correction; Methylated DNA immunoprecipitation; Gene expression","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":[],"consensus_categories":[],"category_scores_codex":[0.001678218,0.0006764715,0.000631313,0.001613925,0.0005342005,0.00125906,0.0007775089,0.0004924193,0.001493919],"category_scores_gemma":[0.003410543,0.0003879148,0.001421902,0.0009562069,0.0003575738,0.0005276288,0.0006962811,0.0007808704,0.0003139336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007036673,"about_ca_system_score_gemma":0.001742535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004531982,"about_ca_topic_score_gemma":0.007082677,"domain_scores_codex":[0.9996517,0.0001174885,0.00001902042,0.00009432216,0.00009695101,0.00002045144],"domain_scores_gemma":[0.9989169,0.0007563485,0.00009481051,0.00007228847,0.0001097351,0.00004996048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004893749,0.000282829,0.03802415,0.0004968816,0.001425127,0.0003711825,0.0002976412,0.7306259,0.03352924,0.01877867,0.004432361,0.1712468],"study_design_scores_gemma":[0.000009091529,0.00001554738,0.001358946,0.000006266061,0.00002950224,0.0000289192,0.0000157119,0.9945033,0.001089662,0.002229086,0.0007064411,0.00000757314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.115722,0.0002158772,0.8786615,0.0002657022,0.00004840485,0.0001133307,0.001018265,0.002940243,0.001014699],"genre_scores_gemma":[0.3073636,0.0001814492,0.6887826,0.0001330041,0.00004168356,0.0003175437,0.001986504,0.0002956333,0.0008978647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004531982,"threshold_uncertainty_score":0.009011209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2677751479555425,"score_gpt":0.4490305208471739,"score_spread":0.1812553728916314,"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."}}