{"id":"W4391115057","doi":"10.1002/ange.202318665","title":"Development of Better Aptamers: Structured Library Approaches, Selection Methods, and Chemical Modifications","year":2024,"lang":"en","type":"article","venue":"Angewandte Chemie","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research - Antimicrobial Resistance Research Initiative; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Aptamer; Selection (genetic algorithm); Computational biology; Biochemical engineering; Computer science; Data science; Biology; Nanotechnology; Engineering; Artificial intelligence; Genetics; Materials science","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":[0.0009800619,0.0007755118,0.0005130573,0.0006021021,0.0001515083,0.0007801176,0.0004362685,0.0005441422,0.000954895],"category_scores_gemma":[0.0006467251,0.000334802,0.0004571202,0.0005996073,0.0003788576,0.0006815438,0.0006196352,0.0009122338,0.0007331751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003916811,"about_ca_system_score_gemma":0.0002061984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001330109,"about_ca_topic_score_gemma":0.000208084,"domain_scores_codex":[0.9994181,0.0001575505,0.00006511411,0.0001402187,0.0001724826,0.0000463867],"domain_scores_gemma":[0.9997634,0.00007440848,0.00005899391,0.00003754608,0.00004932657,0.00001633645],"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":[0.00008678345,0.0001498328,0.000268179,0.0006967799,0.000081461,0.0001443255,0.00006926361,0.003454113,0.9083578,0.003784212,0.0004877779,0.08241951],"study_design_scores_gemma":[0.00003371447,0.0003770425,0.0004093913,0.00005661327,0.00005568892,0.0002631819,0.00001998126,0.004238214,0.9696265,0.001097596,0.02379071,0.00003132794],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3518189,0.07839227,0.5541717,0.001494645,0.000527419,0.0008014616,0.0006146368,0.001176297,0.01100262],"genre_scores_gemma":[0.6135471,0.05198967,0.3186797,0.001555132,0.0001816696,0.0009199766,0.0009438252,0.0002775451,0.01190527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009800619,"threshold_uncertainty_score":0.00518316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02924278580901403,"score_gpt":0.2965898247970773,"score_spread":0.2673470389880633,"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."}}