{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009961819,0.001552209,0.003304793,0.003040561,0.00113774,0.002579477,0.004032221,0.003211423,0.00396887],"category_scores_gemma":[0.02521414,0.001485683,0.002781352,0.003591276,0.002106742,0.002548521,0.001897465,0.00291171,0.0008611559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001750079,"about_ca_system_score_gemma":0.001509055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01918418,"about_ca_topic_score_gemma":0.0173604,"domain_scores_codex":[0.9952408,0.003160965,0.0001473619,0.0006509036,0.0005181029,0.0002818881],"domain_scores_gemma":[0.9836518,0.01438504,0.0007702028,0.0004971444,0.0004763862,0.0002193989],"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":[0.0002868074,0.0001038211,0.006230829,0.0001724349,0.0004383144,0.0002850355,0.0004298522,0.7245564,0.0009040554,0.2299317,0.001796756,0.03486393],"study_design_scores_gemma":[0.00006268578,0.00003108316,0.001170493,0.00002683617,0.00007935312,0.00008955156,0.00002880637,0.8993244,0.0001096002,0.09804818,0.0009908448,0.00003818743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0296548,0.001136238,0.9660547,0.0006621829,0.00005065629,0.0001032474,0.0004682443,0.0002814289,0.00158842],"genre_scores_gemma":[0.5790603,0.002896965,0.3997302,0.0003794309,0.000218937,0.001430247,0.001958279,0.0002197987,0.0141057],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01918418,"threshold_uncertainty_score":0.05268371,"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."}}