{"id":"W2032180763","doi":"10.1002/gepi.20429","title":"Bayesian mixture modeling of gene‐environment and gene‐gene interactions","year":2009,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"National Human Genome Research Institute; National Institutes of Health; Cancer Research UK","keywords":"Curse of dimensionality; Bayesian probability; Set (abstract data type); Gene; Computer science; Computational biology; Multifactor dimensionality reduction; Bayes' theorem; Bayesian hierarchical modeling; Genotyping; Data set; Mixture model; Biology; Genetics; Machine learning; Artificial intelligence","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":[0.0009081206,0.0002260724,0.0005342782,0.0001233217,0.00009741478,0.00001048575,0.0004795973,0.0001750564,0.00001671021],"category_scores_gemma":[0.0001298317,0.0002014905,0.0001153613,0.000114617,0.00007794589,0.00008767757,0.0001308325,0.0002186269,0.000006683846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002954779,"about_ca_system_score_gemma":0.00003495846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002668501,"about_ca_topic_score_gemma":0.000003094273,"domain_scores_codex":[0.9975557,0.0005969749,0.0006550051,0.0006570291,0.00009487768,0.0004404057],"domain_scores_gemma":[0.9985262,0.0002888848,0.0002026684,0.0007553623,0.00003358661,0.0001932794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003141511,0.0001966924,0.002269271,0.00002971717,0.0001229196,0.00003518203,0.001173398,0.08438227,0.09760249,0.05239705,0.0007186205,0.761041],"study_design_scores_gemma":[0.0002187006,0.0001761135,0.003440442,0.0000117084,0.00002656907,0.0001918042,0.000004614883,0.7928768,0.004215631,0.1981116,0.0004988526,0.0002271179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01468991,0.00407653,0.977222,0.003176415,0.0002298492,0.0001668326,0.000004527315,0.00004216879,0.0003918227],"genre_scores_gemma":[0.2710323,0.0006600878,0.7265984,0.001524943,0.0001042479,0.000008572211,0.00000309106,0.000008265901,0.00005999908],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7608138,"threshold_uncertainty_score":0.8216544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03657026031436507,"score_gpt":0.3026487789144117,"score_spread":0.2660785186000466,"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."}}