{"id":"W1576244270","doi":"10.1155/2015/639367","title":"Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks","year":2015,"lang":"en","type":"article","venue":"Advances in Bioinformatics","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Computer science; Bayesian network; Bayesian probability; Data science; Computational biology; Bioinformatics; Artificial intelligence; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003837402,0.001149951,0.001390289,0.004044419,0.0007049261,0.001705449,0.001179857,0.001150936,0.001698501],"category_scores_gemma":[0.01229118,0.0009760918,0.00144273,0.001608366,0.000602826,0.002176648,0.0009956084,0.001396964,0.0004662939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00125464,"about_ca_system_score_gemma":0.001907133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01172707,"about_ca_topic_score_gemma":0.01144026,"domain_scores_codex":[0.9982114,0.0009286617,0.0001140009,0.0003271704,0.000321037,0.00009779569],"domain_scores_gemma":[0.9948943,0.004078352,0.0003799751,0.0001515028,0.0003918249,0.0001040109],"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":[0.0004297278,0.0001938245,0.01939701,0.0002571369,0.0004818275,0.0002384232,0.0001606448,0.7251154,0.001392195,0.02986291,0.002982033,0.2194889],"study_design_scores_gemma":[0.00002337274,0.00001767991,0.0007651683,0.00003189649,0.00004714922,0.00003685815,0.00001291546,0.9606268,0.0003509794,0.03736224,0.0007102982,0.00001472792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03103528,0.001337751,0.9644184,0.0005374554,0.0000367502,0.0001110962,0.0005216378,0.0005178919,0.001483856],"genre_scores_gemma":[0.5434925,0.002285664,0.4488744,0.0004120218,0.0001845767,0.0003448678,0.002092618,0.00007215639,0.002241103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01172707,"threshold_uncertainty_score":0.02331764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04237605167335659,"score_gpt":0.2960356525236013,"score_spread":0.2536596008502447,"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."}}