{"id":"W4416718248","doi":"10.1002/advs.202516852","title":"Aptamer Engineering: Strategies for Discovering Functional Nucleic Acids for Next‐Generation Diagnostics and Biosensing","year":2025,"lang":"en","type":"article","venue":"Advanced Science","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University Health Centre; McGill University","funders":"Fonds de Recherche du Québec - Santé; National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Aptamer; Nucleic acid; Systematic evolution of ligands by exponential enrichment; Biosensor; Nucleic acid detection","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":[0.0001539764,0.0001154925,0.00009864366,0.00008429681,0.0002636254,0.0001155505,0.0000804739,0.00005176851,9.2522e-8],"category_scores_gemma":[0.0003634519,0.0001065557,0.00004599457,0.0002079041,0.0001997863,0.00004312706,0.0000570959,0.00003327621,6.201651e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002133798,"about_ca_system_score_gemma":0.00009914289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001172223,"about_ca_topic_score_gemma":0.000009244303,"domain_scores_codex":[0.9991944,0.000004018257,0.0001303983,0.0003836024,0.00007868897,0.0002088532],"domain_scores_gemma":[0.999554,0.00004162399,0.00004497639,0.0001606197,0.0001622574,0.0000365659],"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":[0.00002221004,0.000009124592,0.00004394006,0.00001394845,0.000008724882,9.928298e-8,0.000007114369,0.001359798,0.9863939,0.003193475,0.00007047066,0.008877187],"study_design_scores_gemma":[0.0002438463,0.0001266388,0.0002913501,0.00002748032,0.00002196225,0.000002382966,0.0001390111,0.02095811,0.9704132,0.0007327133,0.00687639,0.0001669228],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4838158,0.0002431384,0.5154204,0.0001098345,0.000160197,0.0001800575,0.00001141895,0.00002609077,0.0000330465],"genre_scores_gemma":[0.9065705,0.0001615406,0.09285917,0.0001577448,0.0000983012,0.00002487028,0.00003727308,0.000007970664,0.00008261785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4227548,"threshold_uncertainty_score":0.4345217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01906310379480251,"score_gpt":0.2858957212531917,"score_spread":0.2668326174583892,"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."}}