{"id":"W2135338058","doi":"10.1093/bioinformatics/bts154","title":"Bayesian integration of networks without gold standards","year":2012,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"Computer science; Bayesian network; Nexus (standard); Data mining; Data integration; Bayesian probability; Reference data; Gold standard (test); Machine learning; Artificial intelligence; Statistics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01797134,0.002189355,0.002806524,0.007232414,0.001606452,0.005438267,0.004844899,0.002716378,0.005100271],"category_scores_gemma":[0.05893977,0.001883388,0.00202253,0.007033772,0.002011824,0.007012094,0.005525645,0.003840908,0.002860277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003399665,"about_ca_system_score_gemma":0.003040336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01338539,"about_ca_topic_score_gemma":0.01361897,"domain_scores_codex":[0.9871207,0.004617436,0.0008294535,0.003741692,0.003167022,0.0005236004],"domain_scores_gemma":[0.975826,0.01452484,0.001750661,0.003638131,0.003834714,0.0004256827],"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":[0.0005351325,0.0001921638,0.01194085,0.0008431888,0.0005938229,0.0003618761,0.0003790281,0.488483,0.003624985,0.09594413,0.01488333,0.3822185],"study_design_scores_gemma":[0.00004226317,0.00003725399,0.001915417,0.0001326629,0.00008919876,0.0001589667,0.00004911298,0.793999,0.004127114,0.1904359,0.0089649,0.00004831081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007126176,0.0005977734,0.9872279,0.0002507766,0.00004859608,0.0001204614,0.001175398,0.001713215,0.001739696],"genre_scores_gemma":[0.1878954,0.0009422018,0.7926225,0.000407588,0.0001908511,0.0006481518,0.01256974,0.0007254319,0.003998183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01797134,"threshold_uncertainty_score":0.09504265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008798978695646387,"score_gpt":0.2461073266602108,"score_spread":0.2373083479645644,"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."}}