{"id":"W3043995173","doi":"10.1002/chem.202001835","title":"Highly Specific Recognition of Guanosine Using Engineered Base‐Excised Aptamers","year":2020,"lang":"en","type":"article","venue":"Chemistry - A European Journal","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aptamer; Guanosine; Nucleic acid; Chemistry; Biochemistry; Adenosine; DNA; Small molecule; Biophysics; Combinatorial chemistry; Biology; Molecular biology","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":[0.0002497104,0.0003875387,0.0002738804,0.000148475,0.00009934571,0.0002296812,0.0002338445,0.0003190992,0.000473175],"category_scores_gemma":[0.0002842769,0.0001551148,0.0001984444,0.0001132037,0.000185206,0.0001534795,0.0003060178,0.000325144,0.0002898517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001390741,"about_ca_system_score_gemma":0.00009560371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001358204,"about_ca_topic_score_gemma":0.0002799146,"domain_scores_codex":[0.999779,0.00004224288,0.00002087998,0.00006976281,0.00005578348,0.00003234617],"domain_scores_gemma":[0.9998534,0.00004030213,0.00004167074,0.0000198653,0.00002409416,0.00002069691],"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":[0.00001486877,0.000009406464,0.00009039991,0.00001728773,0.000003257146,0.00003158029,0.00001336217,0.0002234624,0.9982293,0.00005641716,0.00001487365,0.001295789],"study_design_scores_gemma":[0.000003599456,0.00008519742,0.000287046,0.000001341059,0.000004493463,0.0000892194,0.000005903921,0.001262318,0.9975677,0.00002049259,0.0006683213,0.000004443383],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9691148,0.0005906036,0.02814314,0.00005098544,0.00004063572,0.00007295996,0.0001302166,0.0001973462,0.001659326],"genre_scores_gemma":[0.9737574,0.0003285309,0.02283315,0.00006425098,0.000009082715,0.00005565026,0.0001822759,0.00003696762,0.002732556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000473175,"threshold_uncertainty_score":0.001582921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.031721788121929,"score_gpt":0.2367230628826113,"score_spread":0.2050012747606823,"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."}}