{"id":"W2791546763","doi":"10.1371/journal.pcbi.1005986","title":"An evolutionary learning and network approach to identifying key metabolites for osteoarthritis","year":2018,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Research and Development Corporation of Newfoundland and Labrador; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Metabolomics; Computational biology; Osteoarthritis; Key (lock); Computer science; Bioinformatics; Metabolite; Set (abstract data type); Machine learning; Biology; Artificial intelligence; Medicine; Biochemistry; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001144008,0.0001222993,0.0002469206,0.00009384072,0.0002594207,0.00002038279,0.00004099796,0.00008251378,0.00002718596],"category_scores_gemma":[0.00006733755,0.0001162772,0.00004339054,0.0001155149,0.00009523407,0.00007240153,0.0000353549,0.00006994011,0.00002979621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001938636,"about_ca_system_score_gemma":0.00004107928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002370031,"about_ca_topic_score_gemma":0.000002151734,"domain_scores_codex":[0.9990694,0.00006863317,0.0001766096,0.0003399944,0.00008723402,0.0002581517],"domain_scores_gemma":[0.9993858,0.0001406607,0.00004619567,0.00006882318,0.0002138133,0.0001447615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002680198,0.001643057,0.1754162,0.0002766042,0.0007099183,0.00002147514,0.00319621,0.002373843,0.1398795,0.3683681,0.001341589,0.3040934],"study_design_scores_gemma":[0.04360795,0.1075124,0.1495262,0.001009658,0.001849653,0.001863019,0.002320451,0.1590393,0.01545139,0.4008734,0.1138423,0.003104282],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9344434,0.002642528,0.05930203,0.0003007495,0.0002921009,0.00114166,0.00005345349,0.0001668932,0.001657185],"genre_scores_gemma":[0.7660012,0.000008916242,0.2318312,0.0003340016,0.0007507589,0.0001161648,0.0008251867,0.00001595669,0.0001166253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3009891,"threshold_uncertainty_score":0.4741647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02733015005989472,"score_gpt":0.2981093558576255,"score_spread":0.2707792057977308,"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."}}