{"id":"W2140183618","doi":"10.2202/1544-6115.1569","title":"Information Metrics in Genetic Epidemiology","year":2011,"lang":"en","type":"article","venue":"Statistical Applications in Genetics and Molecular Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Multiple Sclerosis Society; National Multiple Sclerosis Society; Mitacs; U.S. Department of Defense; National Institutes of Health; National Science Foundation","keywords":"Computer science; Probabilistic logic; Context (archaeology); Relation (database); Interpretation (philosophy); Contrast (vision); Multinomial distribution; Genetic epidemiology; Data mining; Data science; Machine learning; Artificial intelligence; Econometrics; Mathematics; Biology; Genetics; Gene","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":[0.01212405,0.00151762,0.001861566,0.007228054,0.001259016,0.004508645,0.001353715,0.00244937,0.001681713],"category_scores_gemma":[0.03893822,0.0005776561,0.00140921,0.005643471,0.005836205,0.007650652,0.003091202,0.00313358,0.0004180002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003299818,"about_ca_system_score_gemma":0.001434491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001458899,"about_ca_topic_score_gemma":0.0007994186,"domain_scores_codex":[0.9895878,0.007023414,0.0005674603,0.0009606223,0.001551558,0.0003092575],"domain_scores_gemma":[0.961819,0.02946861,0.002430957,0.002520879,0.002926557,0.0008340332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000008151475,0.000006939509,0.0006133465,0.00009520751,0.00004107857,0.00003860586,0.0001192496,0.01186989,0.0001488805,0.9711589,0.001444236,0.01445562],"study_design_scores_gemma":[0.000002836484,0.00001571134,0.0002953296,0.00003792984,0.00001135332,0.00005971169,0.00002550874,0.02066317,0.00006655073,0.9752815,0.003523998,0.00001632402],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01532887,0.02353886,0.943601,0.005282264,0.0004223065,0.00008692344,0.000559564,0.0001493712,0.01103079],"genre_scores_gemma":[0.5657218,0.02731434,0.3935802,0.001796906,0.003388698,0.0006605951,0.00111987,0.0001957085,0.006221948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01212405,"threshold_uncertainty_score":0.06411886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503040580955535,"score_gpt":0.3209507100143086,"score_spread":0.2959203042047532,"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."}}