{"id":"W2869830075","doi":"10.1101/364406","title":"CancerMine: A literature-mined resource for drivers, oncogenes and tumor suppressors in cancer","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency","funders":"Compute Canada","keywords":"Suppressor; Gene; Cancer; Genome; Tumor suppressor gene; Computational biology; Computer science; Biology; Genetics; Carcinogenesis","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":[0.001392905,0.001811405,0.002003838,0.02046504,0.001013502,0.002591407,0.002546416,0.00168003,0.02891836],"category_scores_gemma":[0.008538895,0.0009541183,0.001427499,0.01482262,0.0004624334,0.001743683,0.002353989,0.001216463,0.0170589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00112899,"about_ca_system_score_gemma":0.004071199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0044326,"about_ca_topic_score_gemma":0.009376831,"domain_scores_codex":[0.9990963,0.0001361594,0.0001812333,0.00027346,0.0002549834,0.00005784459],"domain_scores_gemma":[0.9963116,0.002127534,0.0004514118,0.0003851825,0.0004490275,0.000275167],"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":[0.0007588026,0.000158871,0.01150363,0.04719341,0.001043274,0.002771494,0.0008394053,0.006821272,0.02106215,0.01394076,0.7495966,0.1443103],"study_design_scores_gemma":[0.0002460341,0.00009116579,0.00979955,0.003530507,0.0009320685,0.00215644,0.0002183927,0.006917053,0.008818229,0.009801517,0.9573687,0.0001203939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.004218629,0.008597237,0.01369803,0.000665998,0.0001246487,0.0002145132,0.9534258,0.01317714,0.005878062],"genre_scores_gemma":[0.01369114,0.006463363,0.04666404,0.0003551785,0.00009371732,0.0005471029,0.9286573,0.00166708,0.001861024],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02891836,"threshold_uncertainty_score":0.09674156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00834041491738279,"score_gpt":0.2296511136125653,"score_spread":0.2213106986951825,"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."}}