{"id":"W3021581669","doi":"10.1093/bfgp/elaa004","title":"Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data","year":2020,"lang":"en","type":"article","venue":"Briefings in Functional Genomics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Chromosome conformation capture; Chromosome; Computational biology; High resolution; Evolutionary biology; 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":[],"consensus_categories":[],"category_scores_codex":[0.002564355,0.00135113,0.001606953,0.002298471,0.0009066595,0.001880361,0.002850126,0.001700272,0.002781361],"category_scores_gemma":[0.008881087,0.00112486,0.00174278,0.002551506,0.001041103,0.002210557,0.001417065,0.002511045,0.001313105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134816,"about_ca_system_score_gemma":0.001960836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006994046,"about_ca_topic_score_gemma":0.008227358,"domain_scores_codex":[0.9991284,0.0003496178,0.00006350554,0.0001777728,0.0002391715,0.00004158687],"domain_scores_gemma":[0.9955864,0.003569019,0.0002465042,0.0002340254,0.0002718882,0.00009219459],"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":[0.00004582564,0.00005136689,0.001876445,0.00037565,0.0001995747,0.0001060739,0.00006904903,0.8972722,0.001028939,0.02901027,0.00267351,0.06729105],"study_design_scores_gemma":[0.00001153655,0.000007901152,0.0001970124,0.00002294747,0.00001503611,0.00002668389,0.00001725932,0.971707,0.0002764123,0.026408,0.001297088,0.00001308447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004421475,0.001074724,0.9918358,0.000402856,0.00004999467,0.00006548222,0.0003848486,0.000723522,0.00104129],"genre_scores_gemma":[0.07791758,0.003470296,0.9141096,0.000303917,0.0001555421,0.0007191467,0.001751657,0.0003457504,0.001226436],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006994046,"threshold_uncertainty_score":0.01390666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02030182899375265,"score_gpt":0.2506364039273113,"score_spread":0.2303345749335587,"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."}}