{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001109996,0.0007023056,0.0004272887,0.0004569141,0.0002433333,0.0003786964,0.0006544227,0.001007289,0.001217221],"category_scores_gemma":[0.001459122,0.0004974991,0.0004390104,0.0003548231,0.0004864823,0.0005941197,0.000767008,0.0009533482,0.00094042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003780758,"about_ca_system_score_gemma":0.0003527549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00019167,"about_ca_topic_score_gemma":0.0004483432,"domain_scores_codex":[0.9986767,0.0002945028,0.00006428985,0.0003356708,0.000482224,0.0001464887],"domain_scores_gemma":[0.9994162,0.0001858032,0.0001368058,0.00005659629,0.0001365405,0.00006810156],"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":[0.00002848932,0.00000815418,0.00006717801,0.00004301963,0.000002791882,0.0000267651,0.00002487365,0.00007238082,0.9969607,0.0002659781,0.0001444699,0.002355218],"study_design_scores_gemma":[0.000007292467,0.00007564292,0.0002011349,0.000003523553,0.000003136152,0.0001515782,0.000008895214,0.0006734555,0.9970946,0.00007112266,0.001701008,0.000008591011],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5454039,0.006082232,0.4375557,0.000504653,0.000366649,0.0002061882,0.0004883519,0.001709939,0.007682402],"genre_scores_gemma":[0.7479444,0.001961536,0.2408323,0.0003023385,0.0000651046,0.0004162125,0.0007375569,0.00015669,0.007583807],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001217221,"threshold_uncertainty_score":0.005870342,"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."}}