{"id":"W3004940160","doi":"10.1039/d0sc00086h","title":"Engineering base-excised aptamers for highly specific recognition of adenosine","year":2020,"lang":"en","type":"article","venue":"Chemical Science","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; National Institute for Nanotechnology; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aptamer; Adenosine; Biosensor; DNA; Computational biology; Chemistry; Nanotechnology; Base (topology); Computer science; Biochemistry; Biology; Molecular biology; Materials science; Mathematics","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":[0.0002841793,0.0004195265,0.0002367569,0.0002023637,0.0001503883,0.0002692621,0.0002292117,0.0003926129,0.0007598685],"category_scores_gemma":[0.0003419727,0.0002667349,0.0002139087,0.0001476503,0.0002093291,0.0001758398,0.0003175366,0.0005289427,0.000413051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001750328,"about_ca_system_score_gemma":0.0001600039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001623976,"about_ca_topic_score_gemma":0.0005760034,"domain_scores_codex":[0.9997123,0.00005206181,0.0000353851,0.00007289211,0.00009093918,0.00003638468],"domain_scores_gemma":[0.9998333,0.0000470969,0.00004403214,0.0000211626,0.00002602806,0.0000283991],"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":[0.00001234627,0.00001331596,0.00003847867,0.00001147381,0.000003778922,0.00002923071,0.00001254206,0.0001436196,0.9985865,0.00005642588,0.00001685397,0.001075441],"study_design_scores_gemma":[0.00000582557,0.00008149569,0.0003497037,0.000002070753,0.000004493524,0.0001283035,0.000007189978,0.001379336,0.9971076,0.00003040742,0.0008978375,0.00000572296],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.960209,0.0005005305,0.03642971,0.00007646171,0.00007468891,0.0001257114,0.0002067138,0.0002478191,0.002129578],"genre_scores_gemma":[0.9749073,0.0002759158,0.02091946,0.00009225394,0.00001088653,0.00008965425,0.0003179971,0.00006067079,0.003325871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007598685,"threshold_uncertainty_score":0.002542019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02151630789263027,"score_gpt":0.2480765050199444,"score_spread":0.2265601971273142,"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."}}