{"id":"W3007484243","doi":"10.1101/2020.02.20.940205","title":"Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Genome British Columbia; Compute Canada; Genome Canada","keywords":"Computer science; Inference; Betweenness centrality; Visualization; Artificial intelligence; Metabolic network; Leverage (statistics); Embedding; Machine learning; Heuristics; Data mining; Computational biology; Centrality; Biology","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":[0.0005253053,0.0008589301,0.0004050709,0.001547426,0.0002379593,0.0007311006,0.0005377703,0.0004449115,0.001920423],"category_scores_gemma":[0.002585961,0.0002653226,0.0005707782,0.001064569,0.0002814361,0.001255527,0.0007349265,0.0007840747,0.0005705543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004447056,"about_ca_system_score_gemma":0.0004204361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002934613,"about_ca_topic_score_gemma":0.003708625,"domain_scores_codex":[0.9997156,0.00009371872,0.00001027381,0.00009208282,0.00006125492,0.00002706716],"domain_scores_gemma":[0.999145,0.0004790933,0.00008160025,0.0001103519,0.0001343061,0.0000495815],"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":[0.0004316792,0.0001901392,0.01197865,0.0002825931,0.0002004948,0.0002358514,0.0001107195,0.7520385,0.02062262,0.01028034,0.009040845,0.1945875],"study_design_scores_gemma":[0.000004511257,0.00001626428,0.000444539,0.000005645782,0.000007442887,0.00001555091,0.00001015775,0.9922981,0.002038416,0.004488681,0.000665967,0.000004805241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3170455,0.0007480342,0.6628842,0.0006314938,0.00009368789,0.00006468179,0.005174933,0.01040539,0.002952184],"genre_scores_gemma":[0.8176416,0.0002672616,0.1707469,0.00008572691,0.00003449555,0.00006378629,0.009165502,0.0003670451,0.001627622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002934613,"threshold_uncertainty_score":0.006424487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407543841867843,"score_gpt":0.216693528806428,"score_spread":0.2026180903877496,"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."}}