{"id":"W1600834905","doi":"10.1186/1471-2105-6-78","title":"CGMIM: Automated text-mining of Online Mendelian Inheritance in Man (OMIM) to identify genetically-associated cancers and candidate genes","year":2005,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Michael Smith Health Research BC","keywords":"OMIM : Online Mendelian Inheritance in Man; CDKN2A; Cancer; Gene; Mendelian inheritance; Biology; Genetics; Candidate gene; Computational biology; Bioinformatics; Phenotype","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":[0.004488813,0.001851922,0.001239402,0.01197091,0.0008136193,0.001680872,0.001775573,0.0009495766,0.0121445],"category_scores_gemma":[0.01800407,0.0006044346,0.001645942,0.006036262,0.0004428741,0.001436278,0.001848187,0.000754341,0.004495848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008633634,"about_ca_system_score_gemma":0.002066558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002629852,"about_ca_topic_score_gemma":0.004339525,"domain_scores_codex":[0.9979668,0.0005103051,0.000397941,0.0007026901,0.0003413854,0.00008072059],"domain_scores_gemma":[0.9878312,0.008094938,0.001810493,0.0009933438,0.0008912942,0.0003787397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002849139,0.0007049537,0.09342497,0.005501084,0.001534622,0.004517383,0.002366222,0.008360124,0.03380757,0.006514141,0.2291884,0.6112314],"study_design_scores_gemma":[0.00195942,0.001316866,0.1920285,0.001767009,0.001931879,0.01133468,0.001897403,0.3418233,0.07702866,0.03931889,0.3291217,0.0004717472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1453186,0.002850515,0.3439685,0.002253581,0.00044323,0.003698173,0.293784,0.1981401,0.009543291],"genre_scores_gemma":[0.1022668,0.001040753,0.6842239,0.0005387341,0.000263787,0.002216199,0.2026353,0.004027626,0.002786931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0121445,"threshold_uncertainty_score":0.04062742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.020657280603231,"score_gpt":0.3130826807367318,"score_spread":0.2924254001335008,"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."}}