{"id":"W2397929698","doi":"10.1021/jacs.6b01621","title":"<i>In Vitro</i> and <i>In Vivo</i> Enzyme Activity Screening via RNA-Based Fluorescent Biosensors for <i>S</i>-Adenosyl-<scp>l</scp>-homocysteine (SAH)","year":2016,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Cancer-related gene regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Victoria University of Wellington; National Institutes of Health; NIH Office of the Director; University of Victoria; Agilent Technologies; Burroughs Wellcome Fund","keywords":"Chemistry; Biosensor; In vivo; Biochemistry; High-throughput screening; Enzyme; Methyltransferase; Riboswitch; In vitro; RNA; Methylation; Biology; Non-coding RNA; DNA; 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":[0.0004676672,0.0005531642,0.0003689964,0.0002101991,0.0001837266,0.000484482,0.0004855599,0.0006508541,0.001081152],"category_scores_gemma":[0.0005015105,0.0002239824,0.0003602818,0.0001911322,0.0003212205,0.0003638295,0.0002238311,0.000782511,0.0007127136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006099729,"about_ca_system_score_gemma":0.0002052162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006294033,"about_ca_topic_score_gemma":0.0009514911,"domain_scores_codex":[0.9994205,0.0001321505,0.00003761928,0.000168693,0.0001575142,0.00008348534],"domain_scores_gemma":[0.9997409,0.00007379996,0.00008212545,0.00002697796,0.00005500911,0.00002110329],"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":[0.00001658724,0.000009279611,0.00005384425,0.00002807662,0.000002541064,0.00001012483,0.00001276719,0.00009482622,0.9985624,0.0001495909,0.00004549239,0.00101457],"study_design_scores_gemma":[0.00000135498,0.00004114089,0.000125629,0.000001294725,0.000002885285,0.00002352069,0.000006327044,0.0006158879,0.9985601,0.00002580399,0.0005931382,0.000003007085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8128867,0.002916644,0.1698314,0.0007151598,0.0001316465,0.000232495,0.001722276,0.001688774,0.009874807],"genre_scores_gemma":[0.9120228,0.001841942,0.0781361,0.0004821201,0.00005318792,0.0003122762,0.001333899,0.000149945,0.005667654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001081152,"threshold_uncertainty_score":0.004425704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006204904201424705,"score_gpt":0.2311207740416468,"score_spread":0.2249158698402221,"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."}}