{"id":"W2099935683","doi":"10.1101/gr.077578.108","title":"In-depth characterization of the microRNA transcriptome in a leukemia progression model","year":2008,"lang":"en","type":"article","venue":"Genome Research","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; BC Cancer Agency","funders":"National Cancer Institute; Terry Fox Foundation; Deutsche Forschungsgemeinschaft; Michael Smith Health Research BC; Stem Cell Network","keywords":"Biology; Transcriptome; microRNA; Myeloid leukemia; Deep sequencing; Computational biology; Gene expression profiling; Gene; Genetics; Cancer research; Gene expression; Genome","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":[0.0001489553,0.0002962128,0.00035302,0.0003561542,0.0001550263,0.000260697,0.0001531519,0.0001921828,0.0004379725],"category_scores_gemma":[0.0001355179,0.0001289856,0.0003194435,0.0002518852,0.0001290533,0.000159859,0.0001976083,0.0004440258,0.000240733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001981062,"about_ca_system_score_gemma":0.000204733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005671689,"about_ca_topic_score_gemma":0.0007453184,"domain_scores_codex":[0.9998868,0.000009642974,0.000007144936,0.00003750297,0.00004036649,0.00001857488],"domain_scores_gemma":[0.9998972,0.00001931974,0.00003357947,0.00001218574,0.00001848564,0.00001910901],"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":[0.00004656904,0.000009060666,0.000294652,0.00000822634,0.000002369768,0.00001368965,0.00001061927,0.0001307723,0.9989054,0.00002216945,0.000008333468,0.0005480124],"study_design_scores_gemma":[0.00001500903,0.0004148063,0.01575801,0.000004619178,0.00004787132,0.0003367545,0.00004512285,0.01299999,0.9676843,0.0001648634,0.002513118,0.00001563067],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759772,0.0004252061,0.02092201,0.00003469799,0.000009706578,0.0000592804,0.001717079,0.0001567411,0.0006980333],"genre_scores_gemma":[0.9510942,0.0009224145,0.0382733,0.00009735826,0.00001099255,0.0001675461,0.006576783,0.0001154103,0.002741982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005671689,"threshold_uncertainty_score":0.001465201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04303087103324883,"score_gpt":0.3284478139563432,"score_spread":0.2854169429230944,"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."}}