{"id":"W4408332058","doi":"10.1016/j.molcel.2025.02.012","title":"Profiling transcriptome composition and dynamics within nuclear compartments using SLAM-RT&amp;Tag","year":2025,"lang":"en","type":"article","venue":"Molecular Cell","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Natural Sciences and Engineering Research Council of Canada; Howard Hughes Medical Institute","keywords":"Biology; Transcriptome; Computational biology; Profiling (computer programming); Gene expression profiling; Cell biology; Composition (language); Dynamics (music); Genetics; Gene expression; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002013093,0.0002187208,0.0002655904,0.0004273458,0.0002291464,0.0006473989,0.000252423,0.0003546615,0.001150397],"category_scores_gemma":[0.000219138,0.0002933172,0.0003541395,0.0004651069,0.0002287807,0.000259292,0.0002872739,0.0006717505,0.001035659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003545393,"about_ca_system_score_gemma":0.0002074244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005651994,"about_ca_topic_score_gemma":0.001472046,"domain_scores_codex":[0.9997974,0.00001856681,0.00001188956,0.00008183986,0.00005890883,0.0000314133],"domain_scores_gemma":[0.9998369,0.00003869384,0.00004454772,0.00002551098,0.00003558031,0.00001860461],"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":[0.00006613188,0.000008673499,0.0008252509,0.00004686918,0.000008384433,0.00002283461,0.00003308457,0.0001962657,0.9943692,0.00008369363,0.00009245743,0.004247114],"study_design_scores_gemma":[0.00001186113,0.0001122494,0.01913705,0.000009664246,0.0000383333,0.0001832945,0.00006337294,0.01225427,0.9614134,0.000210762,0.006540769,0.00002507241],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8323565,0.003023229,0.1523324,0.0003229748,0.00008476738,0.0001621579,0.007024347,0.001290548,0.003403113],"genre_scores_gemma":[0.8873705,0.001809514,0.09476396,0.0004066858,0.00003330207,0.0004123679,0.008164827,0.0003552245,0.006683576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001150397,"threshold_uncertainty_score":0.003848493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01149653611794138,"score_gpt":0.2722371235775859,"score_spread":0.2607405874596445,"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."}}