{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002478296,0.000180196,0.0002766199,0.0001090723,0.00004532394,0.00002460016,0.0002106226,0.0002360664,0.000008195344],"category_scores_gemma":[0.0002383559,0.0001654326,0.00004127148,0.0002184072,0.0001476397,0.000008422213,0.0001586359,0.00008273018,0.000005170898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005702269,"about_ca_system_score_gemma":0.0001684687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009152959,"about_ca_topic_score_gemma":0.003317353,"domain_scores_codex":[0.9986808,0.0000367475,0.0005761692,0.0001949568,0.0001697535,0.0003415223],"domain_scores_gemma":[0.9993648,0.0000271296,0.0001651603,0.0002336219,0.00006934927,0.0001399631],"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":[0.0005905119,0.0005967629,0.1532703,0.000976795,0.0004348031,0.00001445025,0.00627205,0.00933054,0.2275638,0.00007351611,0.01923945,0.581637],"study_design_scores_gemma":[0.005748695,0.001563236,0.4008483,0.0007338654,0.000117346,0.00004884978,0.006376931,0.4901602,0.05414534,0.00005009782,0.03858386,0.001623364],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965353,0.0009260668,0.001608586,0.0002718826,0.00007389203,0.000177748,0.0001095834,0.0000593758,0.0002375615],"genre_scores_gemma":[0.8240762,0.000495686,0.1745198,0.0005496414,0.00006044533,0.00001500961,0.0001394832,0.00001903967,0.0001246422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5800136,"threshold_uncertainty_score":0.6746144,"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."}}