{"id":"W3012502225","doi":"10.1002/anie.202000025","title":"In Vitro Selection of a DNA Aptamer Targeting Degraded Protein Fragments for Biosensing","year":2020,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Aptamer; Biosensor; Systematic evolution of ligands by exponential enrichment; In vitro; DNA; Chemistry; Computational biology; Selection (genetic algorithm); Biophysics; Molecular biology; Cell biology; Nanotechnology; Biology; Biochemistry; Computer science; Materials science; RNA; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003509034,0.0005784594,0.0003421546,0.0002612409,0.0001389289,0.0003575781,0.0003360647,0.0006119498,0.0007490609],"category_scores_gemma":[0.0005161313,0.0002325227,0.0002614056,0.0002723392,0.000236213,0.0002057775,0.0002860217,0.0006495072,0.0006802785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002605439,"about_ca_system_score_gemma":0.0001782106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003303986,"about_ca_topic_score_gemma":0.0005257069,"domain_scores_codex":[0.9995852,0.00009293941,0.00003571166,0.0001226155,0.0001158449,0.00004767614],"domain_scores_gemma":[0.9996321,0.0001507326,0.00008264778,0.00003618735,0.00005100899,0.00004728538],"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":[0.00000670446,0.000006955113,0.00001970588,0.000008793805,0.000001256615,0.000009491139,0.00000583683,0.00004012125,0.9994036,0.00001778583,0.00001053674,0.000469128],"study_design_scores_gemma":[0.000002383408,0.00006246369,0.0001113359,0.000001369484,0.000003233786,0.00004615914,0.000003987508,0.0005138478,0.9985232,0.00001041354,0.0007194561,0.000002215474],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7819195,0.002349373,0.2103386,0.0003271585,0.0002188489,0.0003198467,0.0004541239,0.0005692571,0.003503148],"genre_scores_gemma":[0.8547013,0.001364399,0.1332638,0.0003422382,0.00004153818,0.0002610143,0.001235482,0.0001534427,0.0086368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007490609,"threshold_uncertainty_score":0.002505839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01423154713661458,"score_gpt":0.2737018736171737,"score_spread":0.2594703264805592,"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."}}