{"id":"W2786122698","doi":"10.1371/journal.pcbi.1005968","title":"Reactome graph database: Efficient access to complex pathway data","year":2018,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":289,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Human Genome Research Institute; University of Toronto; European Bioinformatics Institute","keywords":"Computer science; Graph database; NoSQL; Database; Graph; Graph traversal; View; SQL; Database design; Information retrieval; Big data; Data mining; Theoretical computer science","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":[0.002686674,0.001653331,0.00138533,0.003777198,0.000778224,0.003594588,0.003682877,0.001182228,0.0139549],"category_scores_gemma":[0.006988011,0.001013916,0.001824112,0.004219695,0.0005245961,0.003792763,0.004058867,0.001788919,0.007091222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127835,"about_ca_system_score_gemma":0.00222908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005707368,"about_ca_topic_score_gemma":0.006316426,"domain_scores_codex":[0.9982665,0.0002597522,0.0002180088,0.000437031,0.0007047624,0.0001140016],"domain_scores_gemma":[0.9975255,0.000808979,0.0001599822,0.0008962569,0.0004211715,0.0001881646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003729493,0.0004683524,0.01056345,0.004183881,0.0012029,0.001893749,0.0008927838,0.03746417,0.07452723,0.106035,0.518545,0.2404939],"study_design_scores_gemma":[0.001305066,0.0002309876,0.005566734,0.0003002135,0.0003841621,0.001622823,0.0005077783,0.2664753,0.09043587,0.1442786,0.4884624,0.0004300504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01496303,0.001439201,0.5260526,0.001412835,0.0003641351,0.0005313903,0.1246169,0.3187944,0.01182548],"genre_scores_gemma":[0.1668726,0.003333965,0.4223855,0.001428753,0.0001966979,0.001514184,0.3692107,0.02565113,0.00940646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0139549,"threshold_uncertainty_score":0.04668379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07263716833609722,"score_gpt":0.3247607297612755,"score_spread":0.2521235614251782,"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."}}