{"id":"W2237475663","doi":"10.1016/j.cell.2015.11.062","title":"Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance","year":2016,"lang":"en","type":"article","venue":"Cell","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":497,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"National Institute of General Medical Sciences; National Cancer Institute; National Human Genome Research Institute","keywords":"Biology; Breast cancer; Computational biology; Cancer; Small hairpin RNA; Point mutation; Gene; Genetics; Genomics; Genome-wide association study; Genome; Bioinformatics; Mutation; Single-nucleotide polymorphism; RNA; Genotype","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":[0.0001012639,0.0001338316,0.0002427063,0.000809813,0.0001644212,0.0004695904,0.0001588742,0.0002473182,0.003737826],"category_scores_gemma":[0.0003362933,0.0001240596,0.0001493695,0.0006245589,0.0001959247,0.0001399495,0.0002167455,0.0002031928,0.0003904293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002123878,"about_ca_system_score_gemma":0.0001388158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009035966,"about_ca_topic_score_gemma":0.001179944,"domain_scores_codex":[0.9998817,0.00001513147,0.00000610725,0.00003850037,0.00002867529,0.00002981696],"domain_scores_gemma":[0.9998097,0.00006486914,0.00006727387,0.00001707741,0.00001578373,0.00002548176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002525636,0.0001019139,0.3124212,0.0001906978,0.000325245,0.001600161,0.0003275403,0.002095619,0.6059003,0.005214989,0.001569133,0.06772748],"study_design_scores_gemma":[0.00003538613,0.0002813416,0.939058,0.00002730308,0.0003312742,0.004420378,0.0003765872,0.001987301,0.03846841,0.003996952,0.0109989,0.00001821243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936752,0.002226521,0.0004577583,0.0002807464,0.000004058981,0.000004380646,0.001510932,0.00003605242,0.001804295],"genre_scores_gemma":[0.9983662,0.0004725584,0.0001198418,0.00004222343,0.00000446719,0.000002813635,0.0004898541,0.000008321927,0.0004936447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003737826,"threshold_uncertainty_score":0.01250434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006695832529943527,"score_gpt":0.2053287445523454,"score_spread":0.1986329120224019,"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."}}