{"id":"W3046345618","doi":"10.1101/gr.260844.120","title":"Cross-species analysis of enhancer logic using deep learning","year":2020,"lang":"en","type":"article","venue":"Genome Research","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; National Cancer Institute; Stand Up To Cancer; Centre National de la Recherche Scientifique; KU Leuven; Fonds Wetenschappelijk Onderzoek; Kom op tegen Kanker; Fondation contre le Cancer; Agence Nationale de la Recherche","keywords":"Enhancer; Biology; Computational biology; Chromatin; Enhancer RNAs; Transcription factor; Genetics; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006586789,0.0004201661,0.0003133045,0.0005785886,0.0002944977,0.0004472574,0.0007125302,0.0005583716,0.001568677],"category_scores_gemma":[0.001373935,0.000315492,0.0007318858,0.0002916324,0.0005767407,0.0007396537,0.0006413436,0.001039651,0.0002183594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007787535,"about_ca_system_score_gemma":0.0004426413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003044882,"about_ca_topic_score_gemma":0.004489574,"domain_scores_codex":[0.9998742,0.00003080526,0.000004812356,0.00004466816,0.00002029265,0.00002529018],"domain_scores_gemma":[0.9994911,0.00028459,0.00005917937,0.00006302147,0.00006697906,0.00003509611],"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":[0.0001987383,0.0001198955,0.01071069,0.00011922,0.0001001115,0.0002645586,0.0001342267,0.8721631,0.06092199,0.01565524,0.001026597,0.03858582],"study_design_scores_gemma":[0.000003540134,0.00001325052,0.0006063741,0.000002677506,0.000004296347,0.00001510978,0.00001158017,0.9880235,0.003775306,0.007198032,0.0003428803,0.000003501948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5063313,0.0004772508,0.4876386,0.0002658732,0.00003422736,0.00003155958,0.0007127654,0.002214108,0.002294296],"genre_scores_gemma":[0.9536329,0.0001048232,0.04408304,0.00009416672,0.000008296163,0.00003314787,0.000890203,0.0001405135,0.001012856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003044882,"threshold_uncertainty_score":0.006054282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08121335431884083,"score_gpt":0.3760225299063842,"score_spread":0.2948091755875434,"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."}}