{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006929131,0.000682326,0.0006180253,0.0004747914,0.0001910059,0.0008830968,0.0007162509,0.0009178817,0.002381498],"category_scores_gemma":[0.001266315,0.0004130018,0.001333227,0.000486449,0.0005767233,0.000601612,0.0007363994,0.000955426,0.0005096989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006354586,"about_ca_system_score_gemma":0.0005440465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002714652,"about_ca_topic_score_gemma":0.002062349,"domain_scores_codex":[0.9997768,0.00009809461,0.000007410705,0.00006780231,0.00003178897,0.0000181401],"domain_scores_gemma":[0.9996403,0.0001741961,0.00005517632,0.00008503113,0.00002106305,0.00002410013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001555854,0.0000528677,0.003468672,0.00008110754,0.0001628401,0.000129277,0.00004318988,0.9471354,0.0169342,0.02489154,0.0006525645,0.006292774],"study_design_scores_gemma":[0.00002970381,0.00007284986,0.001743069,0.0000131833,0.00005357149,0.0000898677,0.00002030806,0.9387523,0.005317521,0.04911333,0.004764747,0.00002951149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1852681,0.0006994849,0.8008183,0.0007056322,0.0001345874,0.00006656323,0.005165015,0.001259666,0.005882655],"genre_scores_gemma":[0.8364704,0.00107895,0.1540957,0.0002379235,0.00005206408,0.0002619711,0.002993298,0.000283886,0.004525725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002714652,"threshold_uncertainty_score":0.007966936,"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."}}