{"id":"W4310707155","doi":"10.1038/s41467-022-35017-7","title":"Genetically personalised organ-specific metabolic models in health and disease","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; Chief Scientist Office, Scottish Government Health and Social Care Directorate; NIHR Cambridge Biomedical Research Centre; Economic and Social Research Council; European Commission; 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; Public Health Agency; Government of Canada; Science and Technology Facilities Council; Scottish Government; British Heart Foundation; European Federation of Pharmaceutical Industries and Associations; Dell EMC; Medical Research Council; Scottish Government Health and Social Care Directorate; Wellcome Trust; Research Councils UK; NHS Blood and Transplant","keywords":"Disease; Computational biology; Medicine; Bioinformatics; Biology; Pathology","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":[0.0005994147,0.0007024395,0.0006421727,0.0004982197,0.0002307255,0.000905673,0.0007527923,0.0008275739,0.002072819],"category_scores_gemma":[0.001267885,0.0004282727,0.001419276,0.0004772161,0.000617476,0.0007142425,0.0007878979,0.0009694471,0.0004515715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006893151,"about_ca_system_score_gemma":0.0006497211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003518274,"about_ca_topic_score_gemma":0.003224887,"domain_scores_codex":[0.9997842,0.00009208562,0.00000801895,0.00006743247,0.00003009374,0.00001832083],"domain_scores_gemma":[0.9996402,0.0001682071,0.00005892046,0.00008284399,0.00002386803,0.00002604774],"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":[0.0001726449,0.00006888931,0.004432168,0.0000917034,0.0001748789,0.0001582641,0.00006424926,0.9312605,0.02100275,0.03338213,0.0005482364,0.008643656],"study_design_scores_gemma":[0.00003118945,0.0001118786,0.002573879,0.00001736047,0.00007996905,0.0001285761,0.00003393842,0.9276468,0.004546386,0.058959,0.005830423,0.00004063657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1748568,0.0007738941,0.8122444,0.0005867997,0.0001324041,0.00008096634,0.003290926,0.0008098495,0.007223967],"genre_scores_gemma":[0.8348302,0.00147621,0.1561988,0.0002531122,0.0000486731,0.0003178159,0.002129268,0.0002363664,0.004509628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003518274,"threshold_uncertainty_score":0.006995618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447524130138335,"score_gpt":0.2595902561442451,"score_spread":0.2451150148428618,"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."}}