{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001044388,0.0002914442,0.0003346756,0.001150545,0.0002992735,0.0005429576,0.0004178452,0.000319095,0.0009674688],"category_scores_gemma":[0.002670218,0.0002335165,0.0006317297,0.000809862,0.0005117096,0.0004035356,0.0007022801,0.0008384123,0.0001687852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003105948,"about_ca_system_score_gemma":0.0002562428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001644155,"about_ca_topic_score_gemma":0.004227218,"domain_scores_codex":[0.9996029,0.0001003317,0.00002571531,0.0002073343,0.00004093021,0.00002288802],"domain_scores_gemma":[0.9984231,0.001114371,0.0002284467,0.000120226,0.00005966024,0.00005430972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000412367,0.0001919998,0.4375458,0.0005517994,0.001771495,0.000586717,0.0004960831,0.1738431,0.1862545,0.008882741,0.001380924,0.1880824],"study_design_scores_gemma":[0.00003711714,0.0001592231,0.2705685,0.00006831472,0.0003144798,0.0005414109,0.0001560803,0.6708746,0.02251088,0.03054709,0.004149593,0.00007275685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7378591,0.0009117274,0.2556697,0.0002445445,0.00002151108,0.00002975689,0.002251869,0.00141062,0.001601058],"genre_scores_gemma":[0.9603001,0.0001858771,0.03735057,0.0000824095,0.000009017343,0.0000234407,0.001710414,0.0001282822,0.0002098913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001644155,"threshold_uncertainty_score":0.005523324,"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."}}