{"id":"W2117684318","doi":"10.1002/gepi.20657","title":"Regression and data mining methods for analyses of multiple rare variants in the Genetic Analysis Workshop 17 mini‐exome data","year":2011,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto; Mount Sinai Hospital","funders":"National Institute of General Medical Sciences; National Institute on Drug Abuse; National Institute of Mental Health; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Locus (genetics); Biology; Heritability; Genetic heterogeneity; Computational biology; Genetics; Exome; Population; Missing heritability problem; Allele; Exome sequencing; Phenotype; Genotype; Gene; Genetic variants; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006939329,0.0002979858,0.001037408,0.0002285089,0.0001231829,0.000006592982,0.001893888,0.0005202086,0.00003203233],"category_scores_gemma":[0.01370757,0.0002138548,0.0001488954,0.0004849402,0.0002997799,0.00000855388,0.001206524,0.0001576258,8.47198e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008701943,"about_ca_system_score_gemma":0.00008508112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004014251,"about_ca_topic_score_gemma":0.000888008,"domain_scores_codex":[0.9935989,0.003129883,0.001267846,0.001319506,0.00007902682,0.0006048608],"domain_scores_gemma":[0.992313,0.003469511,0.0007186776,0.003288807,0.0001064887,0.0001034851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001973006,0.0001416375,0.922987,0.00004218573,0.001244962,0.00000397744,0.0004950322,0.0008928392,0.00808117,0.00001819983,0.007431006,0.05846465],"study_design_scores_gemma":[0.0006946446,0.0002731451,0.9342971,0.00001835536,0.001033949,0.00002611747,0.0007241415,0.05832948,0.0002177171,0.0009565844,0.003150922,0.0002778246],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.559365,0.01572613,0.423147,0.0005431031,0.0001733099,0.0005286881,0.0003960925,0.00000777261,0.0001128847],"genre_scores_gemma":[0.4250169,0.0015111,0.5713323,0.0004898529,0.000112835,0.00005123799,0.001413414,0.00001740505,0.00005498308],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1481853,"threshold_uncertainty_score":0.9946004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3416511635213392,"score_gpt":0.4816501297996197,"score_spread":0.1399989662782806,"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."}}