{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002681485,0.0001710396,0.0001739992,0.0001290192,0.0000618909,0.0001631513,0.0006568264,0.0000722007,0.000001481687],"category_scores_gemma":[0.00003056099,0.0001565679,0.00003702931,0.0005240716,0.00006746953,0.002210511,0.0002243105,0.0001453675,0.00001260678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006222949,"about_ca_system_score_gemma":0.00009365974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002581156,"about_ca_topic_score_gemma":0.00001915445,"domain_scores_codex":[0.998697,0.00002910076,0.0004525137,0.0001809819,0.0002465316,0.0003938095],"domain_scores_gemma":[0.9991787,0.00004065482,0.0001404706,0.0004288421,0.00005208429,0.0001592552],"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":[0.000007415601,0.0000243587,0.002527192,0.00002162385,0.00002086707,0.00001231824,0.001300573,0.7560647,0.000005623526,0.00497024,0.00006279735,0.2349823],"study_design_scores_gemma":[0.0002202349,0.0000347203,0.00008586527,0.00007649739,0.000007039628,0.00002510692,0.0001689488,0.9947463,0.00004695089,0.00375256,0.0006074735,0.000228316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002635342,0.004462931,0.9895611,0.0000411097,0.000558459,0.0001040352,9.769476e-7,0.00009111256,0.002544924],"genre_scores_gemma":[0.4398534,0.0003130547,0.5596209,0.0001600613,0.00003608973,0.00000404645,0.000001628638,0.000007569854,0.000003254444],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4372181,"threshold_uncertainty_score":0.6384655,"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."}}