{"id":"W4367173587","doi":"10.1126/science.abm7993","title":"Relating enhancer genetic variation across mammals to complex phenotypes using machine learning","year":2023,"lang":"en","type":"article","venue":"Science","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Drug Abuse; Directorate for Biological Sciences; National Institutes of Health; Uppsala Universitet; University of East Anglia; Broad Institute; Lehigh University; Carnegie Mellon University; Texas Tech University; National Human Genome Research Institute; University of Southern California; Genome British Columbia; Alfred P. Sloan Foundation; University of Nevada, Las Vegas; National Institute of Mental Health; Science for Life Laboratory; Vetenskapsrådet; National Science Foundation","keywords":"Enhancer; Phenotype; Biology; Gene; Genetics; Computational biology; Evolutionary biology; Gene expression","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":[0.0004461351,0.0000801048,0.00006811662,0.00004352113,0.0004091996,0.00007552788,0.0002328836,0.00003917483,0.00001104586],"category_scores_gemma":[0.0001650322,0.00008201058,0.0000236115,0.0004619693,0.0000806527,0.00000508223,0.0002892832,0.00005353182,0.00003640978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002932404,"about_ca_system_score_gemma":0.00006814185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005164289,"about_ca_topic_score_gemma":0.00005030376,"domain_scores_codex":[0.9990481,0.00002015296,0.000137984,0.0003201433,0.0001572505,0.0003163981],"domain_scores_gemma":[0.9996068,0.000007804581,0.00006248811,0.0001829062,0.00007146065,0.00006850695],"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":[0.000003007663,0.000003359898,0.003305047,0.00000431474,0.000002625166,6.313808e-7,0.0003469513,0.04816223,0.9461786,0.00002020094,0.000005203346,0.001967886],"study_design_scores_gemma":[0.0002470428,0.0001558314,0.1984141,0.00002042674,0.000008159148,0.00001320553,0.0002810941,0.6359852,0.1609377,0.00029222,0.003285087,0.0003600109],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880018,0.00004940416,0.01148204,0.00004773837,0.0001340014,0.00008698161,0.00001184093,0.00002190333,0.0001642388],"genre_scores_gemma":[0.9874918,0.00001693397,0.01193332,0.00007527798,0.00008435021,0.000003859455,0.00002132631,0.00001234656,0.0003607681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7852409,"threshold_uncertainty_score":0.3344294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074775672162986,"score_gpt":0.3006698831640945,"score_spread":0.2799221264424647,"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."}}