{"id":"W4293555592","doi":"10.1093/nar/gkac732","title":"Exploration and analysis of R-loop mapping data with <i>RLBase</i>","year":2022,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Cancer Institute; National Institute on Aging; Cancer Prevention and Research Institute of Texas; U.S. Department of Defense","keywords":"Loop (graph theory); Biology; Loop fusion; Data mapping; Human-in-the-loop; Computer science; Data mining; Database; Computational biology; Programming language; Artificial intelligence","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":[0.006553595,0.002512189,0.001785441,0.007430219,0.001833655,0.003371198,0.003505232,0.001347768,0.01252249],"category_scores_gemma":[0.01751748,0.00109274,0.002062841,0.008382022,0.0007093722,0.002690145,0.004604293,0.0030663,0.0153412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038246,"about_ca_system_score_gemma":0.002915037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004668226,"about_ca_topic_score_gemma":0.007406729,"domain_scores_codex":[0.9954661,0.0006432236,0.0006543674,0.001603103,0.001354598,0.0002786459],"domain_scores_gemma":[0.9934304,0.002348455,0.0006945119,0.002123549,0.001053934,0.0003490404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001991396,0.0006055182,0.03022949,0.008359735,0.001227473,0.001522776,0.00190818,0.008707264,0.06569959,0.01399685,0.7074317,0.1583201],"study_design_scores_gemma":[0.0006437028,0.0003499523,0.02547566,0.001233683,0.0004782676,0.001050101,0.0009009037,0.03251224,0.0853578,0.03188718,0.8196747,0.0004357921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.03140903,0.003268744,0.07734989,0.001096402,0.0005111422,0.000562845,0.7379648,0.1378025,0.01003469],"genre_scores_gemma":[0.02564654,0.001011199,0.1484212,0.0005564751,0.00006816453,0.00100219,0.8110488,0.01077643,0.001469004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01252249,"threshold_uncertainty_score":0.04189187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1070766694448749,"score_gpt":0.3597639690653048,"score_spread":0.2526872996204299,"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."}}