{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009239793,0.00009773709,0.000112538,0.0000534081,0.00002670383,0.00001052285,0.00007080822,0.00009666778,0.00000377977],"category_scores_gemma":[0.000216991,0.0000981097,0.00008683877,0.00008842703,0.00003244295,0.00001372185,0.0000281982,0.0000589387,4.505487e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003001108,"about_ca_system_score_gemma":0.00001935007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008931957,"about_ca_topic_score_gemma":0.000003655594,"domain_scores_codex":[0.9992826,0.00001307157,0.0002299201,0.0002381799,0.0001273143,0.0001088884],"domain_scores_gemma":[0.9995867,0.000008624679,0.000147419,0.00004909433,0.0001771029,0.00003107203],"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":[0.0002574128,0.00004015078,0.00005837107,0.00001733616,0.00003727067,6.749577e-7,0.0000246806,0.000003821463,0.9973085,0.000006703044,0.001735285,0.0005098294],"study_design_scores_gemma":[0.0003975383,0.00008406008,0.00002638674,0.00002692073,0.00001094598,0.000001964727,0.00004094723,0.0004513785,0.9943899,0.0001265628,0.004338983,0.0001043936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9561035,0.00004255618,0.04074093,0.002235068,0.0001678625,0.0003460669,0.0001003016,0.000034311,0.0002293837],"genre_scores_gemma":[0.9852017,0.00001779849,0.01276207,0.0004045722,0.0007676085,0.00002270176,0.0007540462,0.00001236923,0.0000571957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02909812,"threshold_uncertainty_score":0.4000798,"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."}}