{"id":"W4413922822","doi":"10.1016/j.jmb.2025.169413","title":"Assessing In Silico Tools for Accurate Pathogenicity Prediction in CHD Nucleosome Remodelers","year":2025,"lang":"en","type":"article","venue":"Journal of Molecular Biology","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Jewish General Hospital; Concordia University; Université de Montréal; Université du Québec à Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; McGill University","keywords":"In silico; Pathogenicity; Computational biology; Biology; Nucleosome; Genetics; Computer science; DNA; Microbiology; Histone; 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":[0.004921966,0.00154716,0.001156449,0.002752649,0.0008045994,0.002038606,0.001306441,0.001347367,0.003099153],"category_scores_gemma":[0.01779429,0.0005517298,0.001687029,0.001091985,0.0003593474,0.001164784,0.001329232,0.0009007118,0.001234634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005631069,"about_ca_system_score_gemma":0.001425351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00213991,"about_ca_topic_score_gemma":0.004379722,"domain_scores_codex":[0.9976498,0.001035594,0.0001982804,0.0003518701,0.0005913379,0.000173095],"domain_scores_gemma":[0.9829324,0.01432341,0.0007041955,0.0006977146,0.000962641,0.0003796626],"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":[0.005427099,0.001730215,0.2710837,0.00483992,0.005080844,0.003256171,0.0004741532,0.446479,0.06070413,0.008403989,0.01922862,0.1732921],"study_design_scores_gemma":[0.0002873747,0.0006795297,0.01109585,0.0002174525,0.001083048,0.0007981459,0.0001617903,0.9603184,0.01549158,0.005193009,0.004617977,0.00005578462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.787418,0.004404844,0.1662364,0.0009976261,0.0003732027,0.0003900801,0.01324944,0.02031877,0.006611682],"genre_scores_gemma":[0.8794179,0.000798257,0.1028579,0.0002514899,0.00007006055,0.0001164837,0.01536911,0.0005650485,0.0005538025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004921966,"threshold_uncertainty_score":0.02603018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813862163529138,"score_gpt":0.3256956308066886,"score_spread":0.3075570091713972,"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."}}