{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002099657,0.0001576108,0.0001588855,0.00008102671,0.0001330301,0.00004736818,0.0009261738,0.000113723,0.0000741539],"category_scores_gemma":[0.00006689139,0.0001425564,0.00003445633,0.0001625335,0.0001742472,0.000005446154,0.001298091,0.000078839,0.0001119518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001025808,"about_ca_system_score_gemma":0.00009249147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001353729,"about_ca_topic_score_gemma":0.00003350613,"domain_scores_codex":[0.9987869,0.00005271898,0.0002900744,0.0004705061,0.0001084317,0.0002913314],"domain_scores_gemma":[0.9988778,0.00004361711,0.0000988336,0.0006715708,0.0001795301,0.0001286983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007867719,0.001016138,0.005398772,0.0001123375,0.0009244127,0.000009729076,0.0003560149,0.02156335,0.7175019,0.02206837,0.1816995,0.04856269],"study_design_scores_gemma":[0.002830539,0.002247577,0.02532413,0.00008397416,0.00008858267,0.0001100824,0.00009329944,0.3714468,0.01915173,0.01501633,0.5616874,0.001919506],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5671369,0.0002208538,0.4221843,0.001730864,0.0006789364,0.000696472,0.003891206,0.0000610999,0.003399343],"genre_scores_gemma":[0.9671851,0.000008149775,0.0137401,0.003553808,0.0006480676,0.00001318019,0.01480363,0.00001605928,0.00003189948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6983502,"threshold_uncertainty_score":0.5813283,"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."}}