{"id":"W2312491920","doi":"10.1142/9789814749411_0032","title":"BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION","year":2015,"lang":"en","type":"article","venue":"","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Bayesian probability; Stratification (seeds); Personalized medicine; Intuition; Probabilistic logic; Bayesian network; Machine learning; Biclustering; Risk stratification; Artificial intelligence; Data mining; Cluster analysis; Bioinformatics; Psychology; Medicine","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":[0.01801616,0.001623748,0.002360889,0.002208817,0.001326481,0.001275935,0.001890977,0.001222385,0.002241634],"category_scores_gemma":[0.0532339,0.0008573581,0.001531327,0.002479474,0.001272085,0.001334233,0.002410916,0.002315426,0.001064377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008501949,"about_ca_system_score_gemma":0.003179342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005925008,"about_ca_topic_score_gemma":0.006523414,"domain_scores_codex":[0.9869843,0.009736485,0.0007342218,0.0009809367,0.001266997,0.0002972772],"domain_scores_gemma":[0.9703296,0.02244837,0.001349117,0.002232009,0.003109936,0.0005309242],"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":[0.001442918,0.0003272754,0.02307455,0.0006752963,0.001114474,0.0002950999,0.001015282,0.6089895,0.003090853,0.04626497,0.01271557,0.3009943],"study_design_scores_gemma":[0.00008345103,0.00006423363,0.001437494,0.00005230272,0.00005027754,0.00005061276,0.00007464931,0.9554389,0.0006839132,0.04073005,0.001301188,0.00003304122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01595674,0.0004521501,0.9813074,0.000505375,0.0000577092,0.0002102633,0.0003900356,0.0005590187,0.0005614143],"genre_scores_gemma":[0.2394138,0.0004354814,0.7539418,0.0006248233,0.0001278331,0.000964897,0.003311926,0.0002242664,0.0009551921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01801616,"threshold_uncertainty_score":0.09527969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02883612309326382,"score_gpt":0.2840827525771511,"score_spread":0.2552466294838873,"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."}}