{"id":"W2100263437","doi":"10.1002/humu.20889","title":"Locus-specific databases and recommendations to strengthen their contribution to the classification of variants in cancer susceptibility genes","year":2008,"lang":"en","type":"article","venue":"Human Mutation","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Medical Research Council; National Cancer Institute; National Institutes of Health; KWF Kankerbestrijding; National Health and Medical Research Council; Ente Cassa di Risparmio di Firenze; National Human Genome Research Institute; Canadian Breast Cancer Research Alliance; Lake Champlain Cancer Research Organization","keywords":"CDKN2A; MLH1; Biology; Locus (genetics); MSH2; Database; Annotation; Consistency (knowledge bases); Genetics; Gene; Disease; Computational biology; Cancer; Colorectal cancer; DNA mismatch repair; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1493642,0.001921006,0.003124777,0.01280494,0.002650309,0.01600611,0.01106469,0.01913214,0.02478513],"category_scores_gemma":[0.3644706,0.002336201,0.00401472,0.0106274,0.005060731,0.02796201,0.01089283,0.02490273,0.02952698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006146321,"about_ca_system_score_gemma":0.03366509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02347991,"about_ca_topic_score_gemma":0.0291005,"domain_scores_codex":[0.9147676,0.03474285,0.02491556,0.004092783,0.01889317,0.00258805],"domain_scores_gemma":[0.4095879,0.1594318,0.02685824,0.04812713,0.3212512,0.03474369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001139861,0.0001337754,0.002670981,0.001302934,0.00008238458,0.0001735268,0.0004937725,0.0004097716,0.0004294229,0.007049096,0.8832274,0.1039128],"study_design_scores_gemma":[0.0001041821,0.00005381699,0.002037474,0.004791799,0.00009756493,0.0002247048,0.0008123068,0.000560489,0.0003902452,0.01198693,0.9788107,0.0001297889],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.00129241,0.01617971,0.0309967,0.8750749,0.04516784,0.001206759,0.005926614,0.003403652,0.02075142],"genre_scores_gemma":[0.008753948,0.04721285,0.4105009,0.4408124,0.02614452,0.002506959,0.02960326,0.002482234,0.03198294],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1493642,"threshold_uncertainty_score":0.7899226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06157825752674494,"score_gpt":0.3148518748320259,"score_spread":0.2532736173052809,"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."}}