{"id":"W1806732830","doi":"10.1186/s13059-015-0723-0","title":"Epigenome data release: a participant-centered approach to privacy protection","year":2015,"lang":"en","type":"article","venue":"Genome Biology","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency; McGill University and Génome Québec Innovation Centre; University of British Columbia; McGill University; McGill Genome Centre","funders":"Canadian Institutes of Health Research; Deutsches Zentrum für Lungenforschung; Knut och Alice Wallenbergs Stiftelse; Interreg; European Commission; Vetenskapsrådet; Bundesministerium für Bildung und Forschung; National Institute of General Medical Sciences; European Molecular Biology Laboratory","keywords":"Epigenome; Epigenomics; ENCODE; Biology; Human genetics; DNA methylation; Computational biology; Human genome; Ambiguity; Genome; Genomics; Computer science; 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":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.3182241,0.00152597,0.002665802,0.004435534,0.005872194,0.01768449,0.0086101,0.01017841,0.009455009],"category_scores_gemma":[0.3899628,0.002431185,0.002040032,0.004526066,0.009618437,0.01992268,0.02477477,0.01379957,0.006639474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003337634,"about_ca_system_score_gemma":0.01865675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003579537,"about_ca_topic_score_gemma":0.002535921,"domain_scores_codex":[0.714067,0.1857949,0.0265045,0.0245069,0.04428655,0.004840205],"domain_scores_gemma":[0.4440919,0.2407642,0.02398345,0.2369642,0.04584628,0.008349948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002689089,0.0004026677,0.01661774,0.001191358,0.0004858893,0.002156511,0.02317787,0.005209628,0.01371783,0.5862588,0.1312596,0.216833],"study_design_scores_gemma":[0.0006107813,0.000539627,0.003655682,0.001263712,0.000277539,0.001500225,0.004905129,0.01652751,0.01998571,0.3577685,0.5925237,0.0004418061],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005956759,0.0004427546,0.9336633,0.02380007,0.001210767,0.003584834,0.002904354,0.004608939,0.02382827],"genre_scores_gemma":[0.2339123,0.001040907,0.6921939,0.02011512,0.002689821,0.01192477,0.009064779,0.004715185,0.02434311],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9913899,"threshold_uncertainty_score":0.8407511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2313855542974254,"score_gpt":0.3367470996597274,"score_spread":0.1053615453623021,"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."}}