{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041248,0.001477734,0.001757046,0.005539432,0.0006172113,0.002702681,0.003312352,0.001411077,0.003321383],"category_scores_gemma":[0.1477927,0.0005514643,0.001490093,0.002682224,0.00262561,0.002387355,0.002281901,0.001953355,0.0005161854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001938757,"about_ca_system_score_gemma":0.002804319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467577,"about_ca_topic_score_gemma":0.001614079,"domain_scores_codex":[0.9608607,0.02787947,0.001933224,0.003140642,0.005583913,0.0006020144],"domain_scores_gemma":[0.811213,0.165869,0.009678578,0.006501693,0.005316247,0.001421593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003132733,0.0004797505,0.07606779,0.001316583,0.001358231,0.001191624,0.0005143152,0.2879176,0.01193092,0.1011663,0.006238212,0.5086858],"study_design_scores_gemma":[0.0004323976,0.001236386,0.01351997,0.0001773081,0.0003177542,0.001169218,0.0001122553,0.8535731,0.01525497,0.1098493,0.004122467,0.0002347804],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04242365,0.0009497842,0.952934,0.0004637477,0.00006002082,0.0003024111,0.0002676793,0.001014434,0.001584378],"genre_scores_gemma":[0.4694568,0.0002571118,0.5282336,0.0003296402,0.0001685648,0.0004807725,0.0003911195,0.0002040895,0.0004783339],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.041248,"threshold_uncertainty_score":0.2181429,"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."}}