{"id":"W2752174355","doi":"10.1002/ange.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","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"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); Nuclease; Chemistry; Cell biology; Gene knockdown; Beacon; Biology; Molecular biology; RNA interference; RNA; DNA; Oligonucleotide; Biochemistry; Gene; Computer science; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001124046,0.0005873478,0.0003248327,0.0004423422,0.0002151686,0.0003654709,0.0005513053,0.0008695471,0.001265166],"category_scores_gemma":[0.001266799,0.0004343812,0.0003884455,0.000279722,0.0004704779,0.0004774657,0.0006445987,0.00077172,0.0007411866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003871619,"about_ca_system_score_gemma":0.0003405292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002097967,"about_ca_topic_score_gemma":0.0004406603,"domain_scores_codex":[0.9989405,0.000238357,0.00005141741,0.000275402,0.0003623055,0.0001319832],"domain_scores_gemma":[0.9994099,0.0001846158,0.0001381643,0.00005532492,0.0001310122,0.00008084919],"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":[0.00003224832,0.000009005718,0.00007448639,0.0000306302,0.000002667061,0.00002640937,0.0000246114,0.00008756452,0.9975942,0.000261274,0.0001299951,0.001727012],"study_design_scores_gemma":[0.000009232685,0.00007612586,0.0002161117,0.0000031928,0.000002742857,0.0001079622,0.0000090042,0.0008048983,0.9974622,0.00006604343,0.001234486,0.000008015202],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7223046,0.00339838,0.2654166,0.0004105126,0.0002761524,0.000155199,0.0004421895,0.001332796,0.006263607],"genre_scores_gemma":[0.8489722,0.0008016501,0.143764,0.0001796808,0.00003815659,0.0002784123,0.0005090606,0.0000942293,0.005362671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001265166,"threshold_uncertainty_score":0.00594455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284030908082373,"score_gpt":0.2499182076846357,"score_spread":0.237077898603812,"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."}}