{"id":"W4225604045","doi":"10.1093/bioinformatics/btac283","title":"Continuous chromatin state feature annotation of the human epigenome","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Genome British Columbia; Michael Smith Health Research BC; Compute Canada; Genome Canada","keywords":"Epigenome; Chromatin; Epigenomics; Computer science; Annotation; Computational biology; Genome; Biology; Genetics; Artificial intelligence; Gene; DNA methylation; Gene expression","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":[0.0006138526,0.0006082446,0.0004699366,0.001292106,0.000396352,0.0008729263,0.0008328485,0.0007995562,0.004948193],"category_scores_gemma":[0.003280054,0.0003318644,0.0007554704,0.00147788,0.0003652662,0.001135383,0.0009106449,0.0007497758,0.001584125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005228549,"about_ca_system_score_gemma":0.000516415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003603176,"about_ca_topic_score_gemma":0.004335635,"domain_scores_codex":[0.9995428,0.00009662368,0.00002533369,0.0002076826,0.00009885762,0.00002857983],"domain_scores_gemma":[0.9987966,0.0005733967,0.0001174605,0.0002465072,0.0002142298,0.00005172543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001618129,0.0001850781,0.04148674,0.001930555,0.0002055889,0.000647814,0.001266295,0.1988911,0.1076015,0.04180525,0.05091925,0.5534428],"study_design_scores_gemma":[0.0000648161,0.00009639149,0.01838429,0.0001280878,0.00006974513,0.0005233969,0.0001823212,0.8117505,0.05689119,0.06474227,0.04707729,0.00008965313],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05523166,0.0009880834,0.8999705,0.0004616552,0.00007523593,0.0001191272,0.02294576,0.0169512,0.003256823],"genre_scores_gemma":[0.4245428,0.0005367431,0.5242663,0.0002436119,0.0001205054,0.0003171976,0.04479111,0.001460318,0.003721432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004948193,"threshold_uncertainty_score":0.0165534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004797717254827892,"score_gpt":0.2073578379386773,"score_spread":0.2025601206838495,"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."}}