{"id":"W4353032789","doi":"10.1021/jacs.3c00848","title":"Pushing Adenosine and ATP SELEX for DNA Aptamers with Nanomolar Affinity","year":2023,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Chemistry; DNA; Adenosine; Nucleic acid; Nucleotide; Small molecule; Biosensor; Biochemistry; Biophysics; Molecular biology; RNA; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002325042,0.0001078084,0.0002116287,0.00001199723,0.00009187114,0.00001864848,0.0001717242,0.00005310014,1.834357e-7],"category_scores_gemma":[0.0001082775,0.00006302897,0.0002735277,0.0002525627,0.0003483048,0.000004737143,0.00009466282,0.0001420112,1.025474e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002231852,"about_ca_system_score_gemma":0.00003808461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003902835,"about_ca_topic_score_gemma":9.826047e-7,"domain_scores_codex":[0.9993483,0.00002223927,0.0001722633,0.0001468106,0.0001386291,0.0001717009],"domain_scores_gemma":[0.9992824,0.00003731914,0.0003575484,0.0001436045,0.0001176204,0.00006150865],"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":[0.00009242713,0.00001799308,0.001012401,0.00000806718,0.0001382179,6.554874e-7,0.00004421932,0.00000356606,0.9915643,0.000001190747,0.005357421,0.001759543],"study_design_scores_gemma":[0.0002627595,0.0002406082,0.0009851494,0.00002330291,0.00008324275,0.0000495235,0.0003675949,0.00006105978,0.9879392,0.00004516019,0.009829123,0.000113247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971494,0.0000608865,0.0006082268,0.002072184,0.00001908237,0.00006009479,0.000007481721,0.00001401236,0.000008622642],"genre_scores_gemma":[0.9828199,0.0002972381,0.01581993,0.0007935009,0.0001922072,0.000001741151,0.000005717127,0.00001516626,0.00005460628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0152117,"threshold_uncertainty_score":0.2570247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008779866930804884,"score_gpt":0.2621267199221463,"score_spread":0.2533468529913414,"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."}}