{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003502849,0.0002015253,0.0003619693,0.00003137955,0.00005366998,0.00001536698,0.0002504244,0.0001446743,0.000001213898],"category_scores_gemma":[0.0002019178,0.0001342535,0.0004219557,0.0002399493,0.0003241171,0.00001536009,0.00009968186,0.0002125291,1.518659e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001546047,"about_ca_system_score_gemma":0.00008904377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002891787,"about_ca_topic_score_gemma":0.000006195444,"domain_scores_codex":[0.9986616,0.00008119616,0.0003833883,0.0002821975,0.0002436302,0.0003479517],"domain_scores_gemma":[0.9987614,0.0001863146,0.0006024348,0.0002401632,0.00009480319,0.0001149463],"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":[0.0004428344,0.00007860579,0.0006518962,0.0000164513,0.00005760668,0.000001107728,0.00003306772,0.0002057197,0.9898394,2.299795e-7,0.005650032,0.003023062],"study_design_scores_gemma":[0.002134625,0.0001401053,0.0005135618,0.00007612242,0.00003608553,0.00002924467,0.00004027474,0.0007566724,0.989292,0.00002219902,0.006857367,0.0001017019],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903919,0.0003902718,0.006421073,0.002438089,0.0001128011,0.0002019128,0.00003187316,0.000004723116,0.000007329247],"genre_scores_gemma":[0.9953385,0.0001802457,0.003084546,0.0008839279,0.0004048534,0.000008613019,0.000002052963,0.00003162958,0.00006560644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004946598,"threshold_uncertainty_score":0.54747,"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."}}