{"id":"W2951560185","doi":"10.1038/s41592-019-0422-y","title":"CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer","year":2019,"lang":"en","type":"article","venue":"Nature Methods","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":223,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"","keywords":"Suppressor; License; Resource (disambiguation); Cancer; Disease; Database; Computer science; Bioinformatics; Biology; Medicine; Genetics; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001932998,0.002088196,0.002164051,0.02443775,0.001469432,0.002576603,0.002482893,0.002079279,0.02545658],"category_scores_gemma":[0.01200071,0.0008834141,0.002138894,0.0156196,0.0005594576,0.002047337,0.003064917,0.001513149,0.01201355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165512,"about_ca_system_score_gemma":0.005459053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005294318,"about_ca_topic_score_gemma":0.01066844,"domain_scores_codex":[0.9988241,0.0002043627,0.0003271652,0.0003140696,0.0002515753,0.00007875858],"domain_scores_gemma":[0.9938136,0.00410161,0.0006342993,0.0004987193,0.000597708,0.0003540823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001624466,0.0003291663,0.01640502,0.09851734,0.002386505,0.005275886,0.001955593,0.005766763,0.03609233,0.02687269,0.5578263,0.246948],"study_design_scores_gemma":[0.0002809197,0.0001354584,0.009514043,0.005710546,0.002479504,0.003016461,0.0003987083,0.004199557,0.009046044,0.01138978,0.9536982,0.0001308677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.006418672,0.01890646,0.0192084,0.0009551019,0.0002228376,0.0004505468,0.9339123,0.01110279,0.008822817],"genre_scores_gemma":[0.03003469,0.01880267,0.07910238,0.001000054,0.0001788709,0.001374415,0.8645715,0.001955396,0.002980172],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02545658,"threshold_uncertainty_score":0.08516073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01149039921388123,"score_gpt":0.3900107605720499,"score_spread":0.3785203613581687,"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."}}