{"id":"W2763093095","doi":"10.1002/anie.201707366","title":"Specific and Direct Amplified Detection of MicroRNA with MicroRNA:Argonaute‐2 Cleavage (miRACle) Beacons","year":2017,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Princess Margaret Cancer Foundation","keywords":"Molecular beacon; Argonaute; microRNA; Cleavage (geology); Cell biology; Nuclease; Gene knockdown; Beacon; Chemistry; Biology; Molecular biology; RNA interference; RNA; DNA; Oligonucleotide; Biochemistry; Gene; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001036081,0.0001342569,0.0001348586,0.00005467527,0.0001527752,0.00005965035,0.0001642385,0.0001143062,0.000003392575],"category_scores_gemma":[0.0000556549,0.0001176213,0.00006694228,0.00002759984,0.0002286463,0.0000221643,0.00007557258,0.00007867955,0.000001058189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002189744,"about_ca_system_score_gemma":0.00001368009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003186781,"about_ca_topic_score_gemma":0.00005059081,"domain_scores_codex":[0.9992605,0.00001084832,0.0001699391,0.0003058559,0.0001391903,0.000113646],"domain_scores_gemma":[0.9992013,0.0000110211,0.0002770069,0.0002916964,0.0001769088,0.00004204544],"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":[0.0001738533,0.00004417404,0.0005367108,0.000008046445,0.00008225552,0.000003063768,0.00001150382,3.414227e-7,0.9946781,0.00001217826,0.00129821,0.003151515],"study_design_scores_gemma":[0.0003870002,0.0001117808,0.005078424,0.00004496175,0.00002684052,0.00003055159,0.00002329514,0.000003880521,0.9741295,0.00008982947,0.01992972,0.0001442128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806483,0.0002410916,0.01071935,0.0005179989,0.0003584147,0.0001494453,0.0001757684,0.00004042454,0.007149144],"genre_scores_gemma":[0.9964032,0.0005528061,0.001748835,0.00006682056,0.0006100051,0.000007693294,0.0002840125,0.00001506074,0.0003115506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02054864,"threshold_uncertainty_score":0.4796459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01143995469089432,"score_gpt":0.2593388063441173,"score_spread":0.247898851653223,"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."}}