{"id":"W2131186458","doi":"10.1093/nar/gkv865","title":"A predictive modeling approach for cell line-specific long-range regulatory interactions","year":2015,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Institute of Environmental Health Sciences; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Institute on Deafness and Other Communication Disorders; U.S. National Library of Medicine; Alfred P. Sloan Foundation; National Institute on Alcohol Abuse and Alcoholism; National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Enhancer; Biology; Computational biology; Promoter; Chromosome conformation capture; Transcription factor; Chromatin; CTCF; Regulatory sequence; Cis-regulatory module; Genome; Gene; Regulation of gene expression; Genetics; 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":[0.0007452343,0.0008711889,0.0009142159,0.0006125237,0.000463542,0.0007128654,0.00141338,0.0009535363,0.001272497],"category_scores_gemma":[0.001713765,0.0005281729,0.001161548,0.0007976932,0.0004230796,0.0005432239,0.0005683462,0.0009765658,0.0002214768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008285543,"about_ca_system_score_gemma":0.001000468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01236873,"about_ca_topic_score_gemma":0.010751,"domain_scores_codex":[0.9998092,0.0000664611,0.000008156776,0.00005338532,0.00003921646,0.00002368977],"domain_scores_gemma":[0.9989851,0.0007880457,0.00006775489,0.00004736707,0.0000759997,0.00003567212],"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":[0.000006222471,0.000007237169,0.000406905,0.000007514836,0.00001201926,0.00001581993,0.000005001284,0.9971815,0.0004257266,0.0007561081,0.00007574531,0.001100164],"study_design_scores_gemma":[9.315814e-7,0.000002049785,0.00005225409,4.724182e-7,0.000002103269,0.000002199759,0.000001063996,0.9993821,0.00009149503,0.0004212809,0.00004282352,0.00000116913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1739198,0.0004622056,0.8182114,0.0003966504,0.00003398538,0.00007500813,0.001703286,0.001114401,0.004083266],"genre_scores_gemma":[0.8860958,0.0004903802,0.1084175,0.0001519721,0.00003903712,0.0004178211,0.0021932,0.000186739,0.002007538],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01236873,"threshold_uncertainty_score":0.02459347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07543557895100918,"score_gpt":0.325738731706369,"score_spread":0.2503031527553599,"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."}}