{"id":"W2136903085","doi":"10.1039/c4mb00123k","title":"Informative Bayesian Model Selection: a method for identifying interactions in genome-wide data","year":2014,"lang":"en","type":"article","venue":"Molecular BioSystems","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Selection (genetic algorithm); Bayesian probability; Computational biology; Genome; Key (lock); Genome-wide association study; Computer science; Biology; Machine learning; Artificial intelligence; Genetics; Gene; Single-nucleotide polymorphism; Genotype","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":[0.01342251,0.001948324,0.00225505,0.004113813,0.001369289,0.001822974,0.003033624,0.001683207,0.004223574],"category_scores_gemma":[0.03123313,0.00118039,0.003128429,0.003657759,0.001428582,0.001665637,0.002444495,0.003554397,0.001090154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028638,"about_ca_system_score_gemma":0.003856187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009353737,"about_ca_topic_score_gemma":0.01145343,"domain_scores_codex":[0.9923343,0.005565847,0.0002769234,0.0007127968,0.0009237357,0.0001863038],"domain_scores_gemma":[0.9819182,0.01564845,0.0006468328,0.0007887281,0.000764159,0.0002336099],"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":[0.0005853573,0.0002391419,0.01466041,0.0005516277,0.002228019,0.000786072,0.0004436621,0.4843661,0.003872503,0.09584257,0.01822974,0.3781948],"study_design_scores_gemma":[0.00007287873,0.00004275063,0.0006865036,0.00004494217,0.0001017816,0.0001253625,0.00003161757,0.9309012,0.0006956804,0.063901,0.003357959,0.0000383159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001535583,0.0001450053,0.997152,0.000150307,0.00002388294,0.00005707978,0.0001588546,0.0005574793,0.0002199085],"genre_scores_gemma":[0.08237807,0.0005073854,0.9122227,0.0004099673,0.0002065843,0.0008041001,0.00146114,0.0004879847,0.001522125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01342251,"threshold_uncertainty_score":0.07098591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04108746734389481,"score_gpt":0.3493017449136608,"score_spread":0.308214277569766,"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."}}