{"id":"W2078133899","doi":"10.1371/journal.pcbi.1003200","title":"Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms","year":2013,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Azrieli Foundation","keywords":"Single-nucleotide polymorphism; Genome-wide association study; Bootstrapping (finance); Disease; Machine learning; Mendelian randomization; Computer science; Genetic association; Computational biology; Artificial intelligence; Biology; Genetics; Medicine; Mathematics; Genotype; Genetic variants; Econometrics; Internal medicine","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":[0.0001761598,0.0001181704,0.0001349025,0.00004868204,0.0001877897,0.00001877997,0.00007903141,0.0001309409,0.00002526958],"category_scores_gemma":[0.000324292,0.000113289,0.00004417272,0.00005395308,0.0001142715,0.0000054744,0.00007058154,0.00008512574,0.0000147398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001561732,"about_ca_system_score_gemma":0.00005972536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005804736,"about_ca_topic_score_gemma":0.000003649805,"domain_scores_codex":[0.9989253,0.0001999979,0.0002541905,0.0003532072,0.00005837784,0.0002089432],"domain_scores_gemma":[0.9993469,0.0001106479,0.0001800597,0.0001196394,0.0001429004,0.00009984392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001206894,0.000044537,0.9614999,0.000007677356,0.00009734419,2.484353e-7,0.00003140019,0.006892852,0.02633898,0.0003018921,0.0001189921,0.004654087],"study_design_scores_gemma":[0.0002566284,0.00005914787,0.648261,0.000004803426,0.00003020187,0.00000456257,0.00004050791,0.3438475,0.00006632293,0.007173718,0.0001407191,0.0001148614],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.953593,0.0004085644,0.04525131,0.0002927399,0.00008044321,0.0002087811,0.00004828832,0.00001749431,0.00009934894],"genre_scores_gemma":[0.9775219,0.00006868074,0.02136289,0.0002282539,0.0002211098,0.00003704411,0.0005264744,0.00001218341,0.00002146071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3369547,"threshold_uncertainty_score":0.4619793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0415988365395941,"score_gpt":0.2934046264119157,"score_spread":0.2518057898723216,"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."}}