{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008083488,0.0001408648,0.0001373969,0.0001067944,0.0001978131,0.00006689652,0.0003684899,0.0001764386,0.000009610286],"category_scores_gemma":[0.00007909496,0.0001404141,0.0001019748,0.0001402208,0.0001119694,0.000008115887,0.000257197,0.0002840465,0.00001844302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001078254,"about_ca_system_score_gemma":0.0001914576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001200192,"about_ca_topic_score_gemma":0.000008847912,"domain_scores_codex":[0.9984806,0.00009157859,0.0002138998,0.0004620366,0.0003236257,0.0004283099],"domain_scores_gemma":[0.9985895,0.00002076666,0.00004076282,0.0005364225,0.0006048199,0.0002076947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003667985,0.002682474,0.00561003,0.0005410308,0.0004231333,0.00001593531,0.004538618,0.1710882,0.6981792,0.001414386,0.1031753,0.008663638],"study_design_scores_gemma":[0.001653331,0.0009875648,0.0001997886,0.00001650872,0.0000144488,0.00001318714,0.00256973,0.9626294,0.01000116,0.0006512487,0.02094525,0.0003183733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8168033,0.000739981,0.1739206,0.00007490125,0.0001550212,0.000559821,0.00006200697,0.0000199443,0.007664359],"genre_scores_gemma":[0.984047,0.0001257477,0.0124989,0.00002554535,0.0005703527,0.0001581304,0.0002812618,0.00005649693,0.002236554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7915412,"threshold_uncertainty_score":0.572592,"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."}}