{"id":"W2773444544","doi":"10.1002/chem.201704600","title":"Selection and Screening of DNA Aptamers for Inorganic Nanomaterials","year":2017,"lang":"en","type":"article","venue":"Chemistry - A European Journal","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aptamer; Selection (genetic algorithm); Nanomaterials; DNA; Systematic evolution of ligands by exponential enrichment; Nanotechnology; Computational biology; Chemistry; Biology; Computer science; Genetics; Materials science; Artificial intelligence; 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":[0.0006411842,0.0004437044,0.0002965485,0.0005188796,0.0002216306,0.0003242831,0.0003167674,0.0004747409,0.0008431379],"category_scores_gemma":[0.0007664998,0.00024909,0.0002067492,0.0002931489,0.0002992215,0.0001627788,0.0003232568,0.0002871192,0.0006725175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002146305,"about_ca_system_score_gemma":0.0001857726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001337252,"about_ca_topic_score_gemma":0.0002807589,"domain_scores_codex":[0.9992633,0.0002083696,0.00004977452,0.0001714199,0.000246909,0.00006012461],"domain_scores_gemma":[0.9996802,0.0001270687,0.00004082625,0.00003787315,0.00007479199,0.00003914303],"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":[0.00001574818,0.00001475673,0.00008035802,0.00001968417,0.000001584703,0.0000163493,0.000007403504,0.0001368809,0.9974983,0.00007465509,0.00002725942,0.002107084],"study_design_scores_gemma":[0.00000498523,0.000103225,0.0002142169,0.000002847844,0.000002572653,0.00005480259,0.000005602715,0.00098092,0.9973991,0.00006009165,0.001168538,0.000003089407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7433285,0.002376352,0.2457898,0.0003020924,0.0001404934,0.0009908699,0.0005154609,0.0008205026,0.005735966],"genre_scores_gemma":[0.7649149,0.001395851,0.2250156,0.0002174698,0.00003598425,0.0006568549,0.0007824551,0.0001037883,0.006876946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008431379,"threshold_uncertainty_score":0.003390908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503088733326481,"score_gpt":0.2663735493324662,"score_spread":0.2513426619992014,"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."}}