{"id":"W2038019245","doi":"10.2202/1544-6115.1713","title":"A Family-Based Probabilistic Method for Capturing De Novo Mutations from High-Throughput Short-Read Sequencing Data","year":2012,"lang":"en","type":"article","venue":"Statistical Applications in Genetics and Molecular Biology","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"National Institute of General Medical Sciences; National Institutes of Health","keywords":"Genetics; DNA sequencing; Biology; Selection (genetic algorithm); Mendelian inheritance; Mutation rate; Mutation; Computational biology; Genome; Population; Human genome; Throughput; Genomics; Computer science; Gene; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002379585,0.0001866002,0.0001939633,0.00005070167,0.0001040238,0.00002363261,0.0002736729,0.0001744599,0.000005780981],"category_scores_gemma":[0.0001734252,0.0001849476,0.00003521807,0.00007816124,0.0001700171,0.00000300856,0.0001997528,0.00008067721,0.000001196497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003628663,"about_ca_system_score_gemma":0.0001968724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001988308,"about_ca_topic_score_gemma":0.00008179643,"domain_scores_codex":[0.9985725,0.00009655166,0.0002896681,0.000578901,0.0000539313,0.0004085031],"domain_scores_gemma":[0.9989146,0.0001801272,0.00005095274,0.0006101619,0.00007009134,0.0001740685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006745562,0.0003125579,0.008527252,0.00008567766,0.0001343754,0.000005683439,0.00008510971,0.002141807,0.9039914,0.0636925,0.0000832545,0.02087295],"study_design_scores_gemma":[0.007315824,0.001586562,0.09278796,0.0001102927,0.002060527,0.0001255764,0.001504775,0.1626635,0.2069267,0.3973169,0.122944,0.004657312],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2508347,0.001687982,0.7432857,0.00006047496,0.00003789479,0.0005318513,0.003519776,0.000007783816,0.00003380619],"genre_scores_gemma":[0.5877025,0.0000537888,0.4046746,0.0002321917,0.00008146637,0.0003196795,0.006912509,0.00002067198,0.000002564608],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6970647,"threshold_uncertainty_score":0.7541942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03463663899362284,"score_gpt":0.3582648466181984,"score_spread":0.3236282076245755,"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."}}