{"id":"W4393712482","doi":"10.5281/zenodo.8407959","title":"NA12878 and MCF7 data for Profiling Chromatin Accessibility in Humans Using Adenine Methylation and Long-Read Sequencing","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Profiling (computer programming); Computational biology; Chromatin; Methylation; DNA methylation; Computer science; Biology; Genetics; DNA; Gene; Operating system","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":[0.001043375,0.002027877,0.002006292,0.002007922,0.0008853054,0.001594803,0.002911888,0.002464858,0.03295125],"category_scores_gemma":[0.003441018,0.000589156,0.001062459,0.00378283,0.0003845341,0.0005975771,0.001504424,0.001378567,0.04197844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094123,"about_ca_system_score_gemma":0.001894153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01447806,"about_ca_topic_score_gemma":0.02936435,"domain_scores_codex":[0.9989806,0.0001698919,0.00009414528,0.0003407798,0.0002788722,0.0001355601],"domain_scores_gemma":[0.9984933,0.0004906017,0.0001508207,0.0004163547,0.0002776352,0.0001713323],"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":[0.0004839082,0.0000736186,0.002996056,0.002164771,0.0002021674,0.0001701859,0.00006409254,0.001343045,0.003466502,0.0009616429,0.9798471,0.008226797],"study_design_scores_gemma":[0.0005777779,0.00007379799,0.01174041,0.0003279387,0.0001450515,0.0003275874,0.00007086668,0.001137421,0.004289871,0.002695455,0.9785509,0.00006292646],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005190552,0.0001346119,0.0001644912,0.0000365553,0.00001189031,0.000008228469,0.9983382,0.0003446431,0.0004422975],"genre_scores_gemma":[0.0005158045,0.00004124244,0.0002851357,0.00002554793,0.000002169121,0.00004412742,0.9987734,0.00005609635,0.0002563664],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03295125,"threshold_uncertainty_score":0.1102329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1143429436240023,"score_gpt":0.3475719697018998,"score_spread":0.2332290260778975,"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."}}