{"id":"W3082440555","doi":"10.1039/d0an00886a","title":"Aptamer-based strategies for recognizing adenine, adenosine, ATP and related compounds","year":2020,"lang":"en","type":"review","venue":"The Analyst","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"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; Canada First Research Excellence Fund","keywords":"Aptamer; Adenosine; RNA; DNA; Chemistry; Computational biology; Biochemistry; Adenosine triphosphate; Biology; Molecular biology; Gene","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002964744,0.0004295167,0.001048177,0.00009887739,0.000198519,0.0001063956,0.0003299049,0.0003684816,0.00000158474],"category_scores_gemma":[0.00007700572,0.0002736781,0.0006508052,0.0003768989,0.0002107712,0.000004242379,0.000104734,0.0002549033,0.000002671421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000199353,"about_ca_system_score_gemma":0.00016622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009851466,"about_ca_topic_score_gemma":0.00001445277,"domain_scores_codex":[0.9983344,0.0001791217,0.0005210282,0.0005952769,0.0001196052,0.0002505382],"domain_scores_gemma":[0.9987848,0.00009676847,0.0004409071,0.0004947346,0.000106858,0.00007597203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000669486,0.00007602522,0.000003715541,0.003180934,0.002442879,0.00001829469,0.00003794525,0.000008615963,0.003731879,0.0001651741,0.006110587,0.984157],"study_design_scores_gemma":[0.0001547758,0.0001709138,4.927928e-7,0.0007521546,0.002535581,0.00004072068,0.00006559797,0.0002584069,0.0007102601,0.000104493,0.9948163,0.0003902729],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002398835,0.9945803,0.004084118,0.000318426,0.00004335066,0.000475298,0.0001101951,0.00006888222,0.0002953989],"genre_scores_gemma":[0.004242633,0.9905166,0.003042912,0.0001877418,0.0002655844,0.000050414,0.00128159,0.00006835128,0.0003441586],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9887058,"threshold_uncertainty_score":0.9999716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03581461388509716,"score_gpt":0.3307408554911558,"score_spread":0.2949262416060586,"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."}}