{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001224004,0.0001694504,0.0002582563,0.0001241002,0.0001141373,0.00003907558,0.00036773,0.0001382448,0.000004707974],"category_scores_gemma":[0.0006071188,0.0001756377,0.00009333664,0.0001643511,0.00001967066,0.00001285144,0.0001997851,0.0001081829,0.000007193059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004699287,"about_ca_system_score_gemma":0.00007451123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009525278,"about_ca_topic_score_gemma":0.0005959434,"domain_scores_codex":[0.9983962,0.0002654241,0.0004725647,0.0004516633,0.00009269355,0.0003214978],"domain_scores_gemma":[0.9989638,0.00007067482,0.0002045874,0.0005711041,0.0001182643,0.00007159325],"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":[0.00009611686,0.0001464273,0.01887137,0.0002218136,0.0004224114,0.000001659351,0.000557185,0.07642055,0.8924267,0.001237329,0.006580582,0.00301783],"study_design_scores_gemma":[0.0007859418,0.0001186304,0.00247328,0.00003090215,0.00004273427,0.00002304856,0.0002559429,0.9521337,0.01300313,0.0007087043,0.03007714,0.000346831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05998468,0.0001234213,0.9379917,0.0003303984,0.0001565997,0.0004330577,0.00006730283,0.00001572064,0.0008970951],"genre_scores_gemma":[0.9012293,0.0000120612,0.09689647,0.000541436,0.0001185364,0.0001280732,0.000776835,0.00002546627,0.0002718003],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8794236,"threshold_uncertainty_score":0.71623,"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."}}