{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004367479,0.0001481006,0.0002235247,0.00006124602,0.00003013524,0.00001929931,0.0001588176,0.0004796403,0.00001218007],"category_scores_gemma":[0.0004139405,0.0001176801,0.00006122004,0.0001418069,0.0000756189,0.00000241709,0.00008244342,0.0002690803,3.296268e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001775225,"about_ca_system_score_gemma":0.00006053002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002539722,"about_ca_topic_score_gemma":0.0000899433,"domain_scores_codex":[0.9990054,0.0001382503,0.0001357272,0.0004040074,0.00007793647,0.0002386389],"domain_scores_gemma":[0.9994815,0.0001289362,0.0000567013,0.0002192198,0.00005066961,0.00006300161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006727545,0.00005431513,0.01568879,0.0003009991,0.00009041508,0.00001228015,0.000506771,0.00002462157,0.6932029,0.000129022,0.01390716,0.2754101],"study_design_scores_gemma":[0.001171067,0.0002431145,0.0051479,0.0001062543,0.0000182161,0.00001352553,0.0001102798,0.0001757503,0.1635667,0.000124431,0.8291096,0.0002131721],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9359854,0.06012216,0.001200963,0.0009844869,0.0006324741,0.0004206361,0.0001140343,0.0000234261,0.0005163726],"genre_scores_gemma":[0.580371,0.001422156,0.4057487,0.003301606,0.000928343,0.0002659947,0.0002372177,0.00006647971,0.007658509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8152025,"threshold_uncertainty_score":0.4798855,"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."}}