{"id":"W2141685742","doi":"10.1159/000371579","title":"Prioritizing Rare Variants with Conditional Likelihood Ratios","year":2015,"lang":"en","type":"article","venue":"Human Heredity","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; European Commission; Epilepsy Research UK; Canadian Institutes of Health Research; National Institute for Health and Care Research; Cancer Research UK; Wellcome Trust; South London and Maudsley NHS Foundation Trust","keywords":"Ranking (information retrieval); Statistics; Statistical hypothesis testing; p-value; Multiple comparisons problem; False discovery rate; Sequence (biology); Set (abstract data type); Computer science; Mathematics; Biology; Genetics; Artificial intelligence; Gene","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":[],"consensus_categories":[],"category_scores_codex":[0.00008084362,0.0001033245,0.00008490567,0.00001591081,0.0001154143,0.00003553035,0.000101901,0.0000676071,0.00003398577],"category_scores_gemma":[0.00002558207,0.00009105068,0.00003695342,0.00002757895,0.00005294713,0.000004647483,0.00005342811,0.00005238084,0.00002008958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001438976,"about_ca_system_score_gemma":0.0001749387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001515474,"about_ca_topic_score_gemma":0.00006810701,"domain_scores_codex":[0.9993535,0.00002867157,0.0001023208,0.0002270437,0.0001250457,0.0001634068],"domain_scores_gemma":[0.9994552,0.000003101482,0.00004593896,0.0001980916,0.0001357327,0.0001619296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008640083,0.001499228,0.1022392,0.0001660976,0.0006636132,0.000823946,0.000724141,0.0002294518,0.6211591,0.009743794,0.2599275,0.001959956],"study_design_scores_gemma":[0.01552024,0.004560831,0.6301041,0.0001514072,0.0002984207,0.00103116,0.002458869,0.0001778896,0.05564725,0.03266457,0.2545136,0.002871679],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962063,0.0002340433,0.0004882384,0.00005600338,0.0001258418,0.0001237835,0.0001226233,0.0000161652,0.002626952],"genre_scores_gemma":[0.9974555,0.000004836261,0.0003243992,0.0001979121,0.0007818151,0.0000142186,0.0009159395,0.00001529745,0.00029004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5655118,"threshold_uncertainty_score":0.3712939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02400139833496208,"score_gpt":0.2586968578512845,"score_spread":0.2346954595163224,"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."}}