{"id":"W7036130861","doi":"","title":"A Bayesian Group Sparse Multi-Task Regression Model for Imaging Genomics","year":2015,"lang":"en","type":"dissertation","venue":"UVic’s Research and Learning Repository (University of Victoria)","topic":"Learning Styles and Cognitive Differences","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Compute Canada; Alzheimer's Disease Neuroimaging Initiative","keywords":"Bayes' theorem; Bayes factor; Bayesian inference; Bayesian probability; Inference; Imaging genetics; Regression; Bayesian hierarchical modeling; Marginal likelihood","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":[0.005815878,0.001603968,0.002266289,0.001149848,0.0005489505,0.001826643,0.004497146,0.004746549,0.006867754],"category_scores_gemma":[0.01194355,0.001200411,0.001930482,0.002241448,0.001767176,0.002232698,0.001915083,0.003947892,0.002872372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001736719,"about_ca_system_score_gemma":0.002059618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01308899,"about_ca_topic_score_gemma":0.01189554,"domain_scores_codex":[0.9974242,0.001308475,0.00008250251,0.000613204,0.0003470413,0.0002244641],"domain_scores_gemma":[0.996165,0.002645097,0.0003706521,0.0002308128,0.0004590997,0.0001293014],"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":[0.0002041225,0.00009681113,0.001055251,0.0001817188,0.0001448078,0.0001733284,0.000186085,0.8223577,0.001184422,0.1382223,0.005450016,0.03074338],"study_design_scores_gemma":[0.00002252981,0.00002010292,0.0001719019,0.00001371368,0.00001551063,0.00002244613,0.000007360395,0.967071,0.00007227556,0.03123125,0.001336036,0.00001578787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005230185,0.0006420116,0.9897355,0.001386391,0.00008810228,0.00007452576,0.0005651647,0.0003922055,0.00188587],"genre_scores_gemma":[0.4137832,0.002270677,0.5430371,0.001934789,0.0006983784,0.001769906,0.00350204,0.0005426956,0.03246128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01308899,"threshold_uncertainty_score":0.03075767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04973691545503793,"score_gpt":0.3392938203010761,"score_spread":0.2895569048460382,"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."}}