{"id":"W2149216452","doi":"10.1074/jbc.m112.361790","title":"Retinoic Acid Receptors Recognize the Mouse Genome through Binding Elements with Diverse Spacing and Topology","year":2012,"lang":"en","type":"article","venue":"Journal of Biological Chemistry","topic":"Retinoids in leukemia and cellular processes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":162,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut National Du Cancer; Université de Strasbourg; Institute of Genetics; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche; Centre National de la Recherche Scientifique; European Commission","keywords":"Retinoid X receptor; Retinoic acid; Genome; Biology; Retinoid X receptor alpha; Genetics; Retinoid; Inverted repeat; Retinoic acid receptor; Receptor; Gene; Response element; Cell biology; Molecular biology; Computational biology; Nuclear receptor; Gene expression; Transcription factor; Promoter","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":[0.0004579719,0.0001731387,0.0002094747,0.00001063524,0.0001368988,0.00002241995,0.0002419015,0.0002417599,0.0001261142],"category_scores_gemma":[0.0002353578,0.00009345707,0.00008100785,0.00007053754,0.0002945827,0.00001342024,0.0001464117,0.0002903418,0.000002802099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002852135,"about_ca_system_score_gemma":0.00003971154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001179231,"about_ca_topic_score_gemma":2.122804e-7,"domain_scores_codex":[0.9989767,0.00007009961,0.0003148329,0.000183873,0.0001284858,0.0003260048],"domain_scores_gemma":[0.9992434,0.00002854144,0.0003595714,0.0001599853,0.00009480596,0.0001136538],"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":[0.0001677885,0.00004096756,0.03174505,0.00002572764,0.00007931986,0.000005985971,0.00008926617,0.000001390504,0.9671479,0.000003834108,0.0002854762,0.0004072488],"study_design_scores_gemma":[0.0004864979,0.0003264506,0.0005798785,0.00001371769,0.00003806193,0.000472503,0.001092284,3.916894e-7,0.9753358,0.00003070172,0.02145912,0.0001645811],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955968,0.002840907,0.0002613092,0.0001299295,0.00008573025,0.00005808749,0.000005433091,0.000005374064,0.00101639],"genre_scores_gemma":[0.9951184,0.001891831,0.001741538,0.0001570181,0.0006790683,0.000003366163,0.00003064593,0.00001170021,0.0003664323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03116518,"threshold_uncertainty_score":0.3811069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02128399670445929,"score_gpt":0.2505865038465642,"score_spread":0.2293025071421049,"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."}}