{"id":"W2748412021","doi":"10.1038/leu.2017.269","title":"RNA interference efficiently targets human leukemia driven by a fusion oncogene in vivo","year":2017,"lang":"en","type":"letter","venue":"Leukemia","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Precision Nanosystems (Canada); University of British Columbia; BC Cancer Agency","funders":"","keywords":"RNA interference; Leukemia; In vivo; RNA; Oncogene; Fusion gene; Oncogene Proteins; Biology; Cancer research; Virology; Computational biology; Fusion; Genetics; Gene; Regulation of gene expression; Cell cycle","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0001860647,0.0007053544,0.0006406825,0.0001933085,0.0002368756,0.0001640133,0.00168142,0.001853416,0.0002273056],"category_scores_gemma":[0.0000671978,0.0007019548,0.0003112486,0.00007886814,0.0002285804,0.00001245185,0.000666343,0.001492639,0.0001477194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007203759,"about_ca_system_score_gemma":0.0006383064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000803589,"about_ca_topic_score_gemma":0.0003518386,"domain_scores_codex":[0.9967472,0.0001364803,0.000610996,0.00130538,0.0003619876,0.0008379438],"domain_scores_gemma":[0.9977304,0.00001964197,0.0004701814,0.001520998,0.0001407892,0.0001179703],"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":[0.0000283854,0.00001441242,0.0001333772,0.0000436724,0.00004165148,0.0001727566,0.00004670033,0.00002908982,0.4676241,7.337742e-7,0.5314808,0.0003842825],"study_design_scores_gemma":[0.0008672112,0.0005052797,0.0001034836,0.0003515425,0.00004154869,0.00003121977,0.00001792222,0.0001230913,0.4062394,0.00003449505,0.5908293,0.0008554184],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757568,0.001845186,0.0002534239,0.009251546,0.001221635,0.0006084967,0.0004537149,0.00005014097,0.01055904],"genre_scores_gemma":[0.870032,0.0005826128,0.0001087364,0.07916176,0.003447089,0.0001240685,0.003173221,0.0001592706,0.04321123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1057248,"threshold_uncertainty_score":0.9995431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306351804502774,"score_gpt":0.2583927460120305,"score_spread":0.2453292279670028,"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."}}