{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004424519,0.00008956355,0.0001957911,0.0001332407,0.0001417538,0.00004758502,0.0002920841,0.0001017223,0.0002996499],"category_scores_gemma":[0.0001515762,0.00008874902,0.0001414017,0.0007307944,0.0001692166,0.000002156283,0.0003351094,0.000183651,0.00001263773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002895885,"about_ca_system_score_gemma":0.00006710801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002739187,"about_ca_topic_score_gemma":0.00002471805,"domain_scores_codex":[0.9987827,0.0001084142,0.0002120447,0.0003188051,0.0002564079,0.0003216231],"domain_scores_gemma":[0.9993033,0.00002059627,0.00006539921,0.0002327452,0.0002771144,0.0001008167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003183927,0.00001606308,0.01123568,0.00002943164,0.0002975503,0.000002193328,0.0002301853,0.04868299,0.939076,0.00008167383,0.000004439698,0.0003119212],"study_design_scores_gemma":[0.001247033,0.001797493,0.2617661,0.00001674581,0.0004593316,0.000007733526,0.002319037,0.3271102,0.360999,0.000273314,0.04295838,0.001045591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900005,0.001356166,0.007026338,0.00006225709,0.00001401345,0.00008413119,0.00001854743,0.000004904211,0.001433148],"genre_scores_gemma":[0.9973341,0.000501459,0.001326286,0.00003241684,0.0001196641,0.000004136067,0.0001201606,0.00001596828,0.0005458565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.578077,"threshold_uncertainty_score":0.361908,"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."}}