{"id":"W2948909630","doi":"10.1101/661140","title":"Essential gene networks in acute myeloid leukemia identified using a <i>microRNA</i> -knockout CRISPR library screen","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"Leslie Dan Faculty of Pharmacy, University of Toronto; Natural Sciences and Engineering Research Council of Canada; Leukemia and Lymphoma Society of Canada; University of Toronto; Leukemia and Lymphoma Society","keywords":"microRNA; Myeloid leukemia; XIAP; Biology; Signal transduction; Computational biology; Gene; Cancer research; Cell biology; Apoptosis; Genetics; Programmed cell death","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":[0.0001652754,0.0003770965,0.0003637952,0.0007441459,0.0002137096,0.0003264224,0.0002182828,0.0001716889,0.001715329],"category_scores_gemma":[0.0002114803,0.0001931771,0.0004156762,0.0004405998,0.0001462927,0.0001278737,0.0002587792,0.0002607658,0.0003331384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003537482,"about_ca_system_score_gemma":0.000304229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007168169,"about_ca_topic_score_gemma":0.001552959,"domain_scores_codex":[0.999804,0.0000165556,0.00001770655,0.00006029286,0.0000728423,0.00002857656],"domain_scores_gemma":[0.999814,0.00004263371,0.00008341139,0.00001397886,0.00001457429,0.00003146816],"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":[0.0001855367,0.00003256813,0.003684725,0.00009492134,0.00003946727,0.000310396,0.00001459654,0.001906754,0.987381,0.0004226351,0.0002225903,0.005704851],"study_design_scores_gemma":[0.00007334174,0.00041119,0.0613705,0.00002336162,0.0002384005,0.002142338,0.00005723529,0.03195033,0.894735,0.001061702,0.00790759,0.00002894544],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.961774,0.0005046861,0.02908829,0.0001001029,0.00001512164,0.0001018661,0.00519533,0.001452358,0.001768338],"genre_scores_gemma":[0.9705374,0.0003108771,0.02393874,0.00007254915,0.000003555966,0.00007856247,0.003723825,0.00008743339,0.001247035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001715329,"threshold_uncertainty_score":0.005738378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00706652255185521,"score_gpt":0.2377725549698951,"score_spread":0.2307060324180399,"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."}}