{"id":"W3044838252","doi":"10.3390/cancers12082002","title":"Targeting STAT3 and STAT5 in Cancer","year":2020,"lang":"en","type":"editorial","venue":"Cancers","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Research, Development and Innovation Office; Hungarian Scientific Research Fund; Canadian Institutes of Health Research; Austrian Science Fund","keywords":"Computational biology; Cancer; Human genome; Biology; Gene; Genome; Genetics; Cancer research","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":[0.006647964,0.002722577,0.002070324,0.001880338,0.001764532,0.004763808,0.002612101,0.00923187,0.009036261],"category_scores_gemma":[0.01067326,0.0009847695,0.001886993,0.0007869547,0.00178017,0.00416911,0.001839361,0.02041216,0.008435195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002910855,"about_ca_system_score_gemma":0.002723164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001686679,"about_ca_topic_score_gemma":0.005421079,"domain_scores_codex":[0.9971145,0.0005345785,0.0003337446,0.0003662542,0.001422509,0.000228421],"domain_scores_gemma":[0.9908303,0.003821592,0.0004010188,0.0002089013,0.003024733,0.001713539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006986698,0.00001277199,0.00001998078,0.000181791,0.00001620411,0.00005568014,0.000009338075,0.00003231638,0.0001603231,0.0006243794,0.9901224,0.008694946],"study_design_scores_gemma":[0.00008946933,0.00005729299,0.0002037512,0.0003401567,0.00004415389,0.000165103,0.00002151183,0.0001725119,0.0001941334,0.002001842,0.9966912,0.00001882001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00007336459,0.02616583,0.0003086733,0.05135807,0.9198394,0.00002284457,0.0001023389,0.0001118136,0.002017777],"genre_scores_gemma":[0.0007505149,0.02074919,0.0002116443,0.04207017,0.920202,0.00003147834,0.00008083077,0.00006040375,0.01584384],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.00923187,"threshold_uncertainty_score":0.03515822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006864070484919944,"score_gpt":0.2646314888082739,"score_spread":0.257767418323354,"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."}}