{"id":"W3009856650","doi":"10.1101/2020.01.12.20016691","title":"metPropagate: network-guided propagation of metabolomic information for prioritization of neurometabolic disease genes","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary; BC Children's Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; BC Children's Hospital; Michael Smith Health Research BC; Stichting Metakids; Children's Hospital Foundation","keywords":"Candidate gene; Computational biology; Gene; Exome; Exome sequencing; Prioritization; Phenotype; Bioinformatics; Genetics; Biology; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001285539,0.001397468,0.0008794478,0.002693566,0.0006942829,0.001083067,0.001043625,0.000781184,0.004260348],"category_scores_gemma":[0.004456788,0.0004536309,0.001219181,0.0009110082,0.0003452575,0.0006441729,0.001005086,0.0009564616,0.0007898973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009209877,"about_ca_system_score_gemma":0.001414462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008348891,"about_ca_topic_score_gemma":0.01400812,"domain_scores_codex":[0.9995884,0.0001193048,0.00001853321,0.0001287024,0.0001043466,0.00004055653],"domain_scores_gemma":[0.9983377,0.001037736,0.000201558,0.00008631096,0.0002359285,0.0001007098],"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":[0.002192358,0.0005986154,0.06042755,0.0008341551,0.001126637,0.001197396,0.0004268777,0.541712,0.04759709,0.00837279,0.03648053,0.2990339],"study_design_scores_gemma":[0.00008395527,0.00008658661,0.0021531,0.00001352766,0.00006266504,0.00009454698,0.00002438624,0.9893069,0.003535445,0.002924812,0.001698123,0.00001600677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2330504,0.0009446875,0.7209654,0.001635714,0.0002799074,0.0004777049,0.009540432,0.02897646,0.004129426],"genre_scores_gemma":[0.5676448,0.0003830926,0.4136162,0.0005564598,0.0001779754,0.0005492708,0.01243896,0.001321907,0.003311298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008348891,"threshold_uncertainty_score":0.01660055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897770407091269,"score_gpt":0.2526060442895929,"score_spread":0.2336283402186803,"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."}}