{"id":"W2549803051","doi":"10.1182/blood.v110.11.866.866","title":"Accurate Detection of the microRNA Transcriptome in a Leukemia Progression Model.","year":2007,"lang":"en","type":"article","venue":"Blood","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Terry Fox Research Institute","funders":"","keywords":"microRNA; Deep sequencing; Biology; Myeloid leukemia; Transcriptome; Gene expression profiling; Computational biology; Small RNA; DNA microarray; Cancer research; Genetics; Gene; Gene expression; Genome","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":[0.0003800072,0.0004280326,0.0007603752,0.0005553905,0.0002636394,0.0005120382,0.0002351648,0.0003738309,0.000828897],"category_scores_gemma":[0.0003435621,0.0003597174,0.0004002252,0.0005366862,0.0002016359,0.000313606,0.000428944,0.0007210544,0.000702864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003665785,"about_ca_system_score_gemma":0.0002948978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000614082,"about_ca_topic_score_gemma":0.001208157,"domain_scores_codex":[0.999496,0.00003111155,0.00002606612,0.0002204536,0.0001794127,0.00004689146],"domain_scores_gemma":[0.9997621,0.00004222734,0.0000626961,0.0000372741,0.00006213639,0.0000335912],"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":[0.000127552,0.00001485739,0.0008112803,0.00003592607,0.000009012751,0.00001335653,0.00001748004,0.0002137768,0.9952969,0.00004290424,0.00006369995,0.003353307],"study_design_scores_gemma":[0.00002533147,0.0004293334,0.02926741,0.00001413557,0.00009637797,0.0002891771,0.00004632635,0.01886553,0.9450639,0.0002616751,0.005601681,0.00003928836],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8510931,0.002502823,0.1271697,0.00009280506,0.0001041484,0.0003042593,0.01419502,0.001941978,0.00259602],"genre_scores_gemma":[0.8233998,0.001544471,0.1481888,0.0001609745,0.00003543479,0.0007453869,0.0207302,0.0005716726,0.00462315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000828897,"threshold_uncertainty_score":0.002772927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008611776945099785,"score_gpt":0.2519502810959748,"score_spread":0.243338504150875,"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."}}