{"id":"W2151991601","doi":"10.1093/database/bau095","title":"WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions","year":2014,"lang":"en","type":"article","venue":"Database","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"National Center for Chronic Disease Prevention and Health Promotion; U.S. National Library of Medicine; National Institute of General Medical Sciences; National Institutes of Health","keywords":"Computer science; Python (programming language); JSON; Metadata; JavaScript; Database; Interface (matter); Relational database; Graphical user interface; Interoperability; SQL; Relational database management system; Information retrieval; World Wide Web; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003972223,0.0002163618,0.0001765928,0.0000833096,0.0002119932,0.00002561336,0.0002646087,0.00008331684,0.00002949412],"category_scores_gemma":[0.0001281163,0.0002291885,0.0001704854,0.000119604,0.00007209474,0.00001643071,0.0002036568,0.0001022293,0.00006531934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001805703,"about_ca_system_score_gemma":0.000105365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008758178,"about_ca_topic_score_gemma":0.00003247264,"domain_scores_codex":[0.9984834,0.00006639247,0.0002877405,0.0006294881,0.0002010912,0.0003319497],"domain_scores_gemma":[0.9983574,0.0000367283,0.0001182335,0.001160464,0.0001383056,0.0001888826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001072368,0.0002578072,0.0006793439,0.00007515022,0.0001252297,0.000001789039,0.00001230831,0.1126664,0.522112,0.0007697673,0.3626854,0.0005075447],"study_design_scores_gemma":[0.00102448,0.00008485978,0.00005035761,0.00001517747,0.0001854304,0.000007597374,0.00001052638,0.5462779,0.06218841,0.0001498765,0.3896956,0.000309829],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1032562,0.0004930152,0.8879591,0.0002625621,0.0001369526,0.0003469672,0.006574514,0.00004290074,0.0009277683],"genre_scores_gemma":[0.8836184,0.000108494,0.0420424,0.000682688,0.0008318839,0.0001798821,0.06613497,0.00008151512,0.006319764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8459167,"threshold_uncertainty_score":0.9346037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01507083620479464,"score_gpt":0.2365658045377128,"score_spread":0.2214949683329181,"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."}}