{"id":"W4200044848","doi":"10.3390/metabo12010010","title":"TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome","year":2021,"lang":"en","type":"article","venue":"Metabolites","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University","funders":"National Institute of General Medical Sciences; National Institute on Aging; National Institutes of Health; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; McGill University; Heart and Stroke Foundation of Canada","keywords":"Microbiome; Tryptophan; Metabolomics; Biology; Gut microbiome; Metabolic pathway; Metabolome; Metabolism; Indoleamine 2,3-dioxygenase; Computational biology; Crosstalk; Gut flora; Biochemistry; Bioinformatics; Amino acid","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":[0.0007617521,0.001133973,0.0007153251,0.002938772,0.000308997,0.001400328,0.0007397523,0.0008415105,0.00272111],"category_scores_gemma":[0.002209303,0.0003098288,0.0009799784,0.001789191,0.0001893258,0.001737158,0.001424713,0.0004980094,0.00124751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004430404,"about_ca_system_score_gemma":0.0007662814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003686466,"about_ca_topic_score_gemma":0.005809058,"domain_scores_codex":[0.9996608,0.00005529007,0.00003217011,0.0001477417,0.0000754947,0.00002841245],"domain_scores_gemma":[0.99938,0.0002375489,0.0001239587,0.0000887553,0.00008908117,0.00008064748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006392571,0.0006721253,0.2681943,0.01151693,0.002163724,0.002928948,0.000858006,0.09460801,0.1249341,0.01198129,0.1243802,0.3513697],"study_design_scores_gemma":[0.0003668231,0.001160108,0.1506363,0.00106813,0.001098868,0.002822636,0.001110717,0.4925687,0.07019889,0.05306192,0.2255467,0.0003602219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3046096,0.01225473,0.1333425,0.001619227,0.0003292764,0.0003054438,0.4979842,0.04211975,0.007435362],"genre_scores_gemma":[0.4828747,0.007199824,0.1436994,0.0006006409,0.000140203,0.0004563094,0.3616285,0.001168551,0.002232014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003686466,"threshold_uncertainty_score":0.009103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02999698612732876,"score_gpt":0.2986477998086036,"score_spread":0.2686508136812748,"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."}}