{"id":"W4220832357","doi":"10.1101/2022.03.25.22272958","title":"Genetically personalised organ-specific metabolic models in health and disease","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; Canadian Institutes of Health Research; Chief Scientist Office, Scottish Government Health and Social Care Directorate; NIHR BioResource; University of Cambridge; Department of Health and Social Care; Health and Social Care Research and Development Division; National Institute for Health and Care Research; NIHR Cambridge Biomedical Research Centre; Economic and Social Research Council; European Commission; Dell EMC; Science and Technology Facilities Council; Scottish Government; European Federation of Pharmaceutical Industries and Associations; British Heart Foundation; Wellcome Trust; Public Health Agency; NHS Blood and Transplant","keywords":"Biobank; Disease; Biology; Genome-wide association study; Computational biology; Bioinformatics; Genetic association; Leverage (statistics); Genetics; Medicine; Single-nucleotide polymorphism; Gene; Internal medicine; Genotype; Computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003980749,0.0002546131,0.0003109724,0.000111357,0.00006214629,0.00003078705,0.0002191217,0.0001267508,0.00005132546],"category_scores_gemma":[0.00007443999,0.000261935,0.00008710277,0.0001142993,0.00005511642,0.000001967455,0.0004757999,0.0003520846,0.000001616354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002122431,"about_ca_system_score_gemma":0.0002675992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004090811,"about_ca_topic_score_gemma":0.000006531041,"domain_scores_codex":[0.9983256,0.0001513415,0.0003125201,0.0007699311,0.0001577035,0.0002828786],"domain_scores_gemma":[0.9990328,0.000002447158,0.00009213379,0.0005301613,0.00003462005,0.0003078693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005050387,0.0004344774,0.01212929,0.00117108,0.00020128,0.00003195949,0.001029368,0.03916253,0.9269922,0.002992953,0.003549682,0.01180008],"study_design_scores_gemma":[0.002063388,0.0002967861,0.05360702,0.0002206312,0.00008588479,0.00004172796,0.0002599907,0.003927418,0.01806502,0.002610173,0.916752,0.002069997],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9258469,0.07068779,0.001337179,0.001102129,0.0005297635,0.000335473,0.00009018283,0.00002447084,0.00004606028],"genre_scores_gemma":[0.9700523,0.02643885,0.001528756,0.0003674941,0.0005543975,0.00008291217,0.000439674,0.00005429095,0.0004813222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9132023,"threshold_uncertainty_score":0.9999833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685195255080769,"score_gpt":0.2430930896982614,"score_spread":0.2262411371474537,"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."}}