{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00999728,0.002334,0.002609444,0.006218457,0.001217701,0.002452244,0.00235419,0.002401581,0.003073423],"category_scores_gemma":[0.03388641,0.0005595525,0.001614015,0.002990823,0.0008634963,0.001631038,0.001731386,0.002230294,0.00229241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008573928,"about_ca_system_score_gemma":0.001626816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004395016,"about_ca_topic_score_gemma":0.00377696,"domain_scores_codex":[0.9938915,0.003263785,0.0004805152,0.0007575476,0.0011877,0.0004189426],"domain_scores_gemma":[0.9730232,0.02027718,0.001262794,0.002126189,0.002656312,0.0006542287],"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":[0.001028676,0.0006763411,0.04048727,0.0003027457,0.0005146306,0.0004817131,0.0001781827,0.4825867,0.00202525,0.007525294,0.01584105,0.4483522],"study_design_scores_gemma":[0.00005223832,0.00006628123,0.001190997,0.00002026117,0.00002448533,0.00007350271,0.00002244838,0.9868881,0.0004808251,0.01049607,0.0006655017,0.00001933675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1701879,0.00257893,0.8117406,0.001613676,0.000481398,0.0005004969,0.002068078,0.006265723,0.004563211],"genre_scores_gemma":[0.6826092,0.0006841266,0.3070879,0.0005656077,0.0007140318,0.0004448509,0.005742638,0.0003125231,0.001839213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00999728,"threshold_uncertainty_score":0.05287129,"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."}}