{"id":"W2942162969","doi":"10.1038/s41598-019-42671-3","title":"Selection of high affinity aptamer-ligand for dexamethasone and its electrochemical biosensor","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Aptamer; Biosensor; Dexamethasone; Ligand (biochemistry); Selection (genetic algorithm); Electrochemistry; Chemistry; Computational biology; Combinatorial chemistry; Computer science; Biochemistry; Molecular biology; Biology; Medicine; Internal medicine; Electrode; Receptor; Artificial intelligence","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":[0.0004111675,0.0005619098,0.0004246299,0.0002319906,0.0001359154,0.0002575706,0.0004981623,0.0006277364,0.0006585958],"category_scores_gemma":[0.000382174,0.0001807676,0.0001634287,0.0001677967,0.0001848161,0.0001920602,0.0002857372,0.00048054,0.0004604397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003767574,"about_ca_system_score_gemma":0.0001793647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000372882,"about_ca_topic_score_gemma":0.0008308268,"domain_scores_codex":[0.9992913,0.0001696082,0.00004584951,0.0001840642,0.0002455441,0.00006357292],"domain_scores_gemma":[0.9998034,0.00004973216,0.00002898156,0.00001264012,0.00005695653,0.0000483222],"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":[0.000007553667,0.000007727289,0.00003934539,0.00001186015,8.247513e-7,0.00001086009,0.000003505793,0.00002182131,0.9992704,0.0000190634,0.00001356395,0.0005935009],"study_design_scores_gemma":[0.00000581343,0.00004529082,0.0002488953,0.00000176802,0.000002752037,0.00008538032,0.000004884337,0.0007962653,0.998007,0.00001148916,0.0007860932,0.000004333883],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8106786,0.005306201,0.1785848,0.0005618911,0.0002403705,0.0004448008,0.0004587958,0.0007670384,0.002957452],"genre_scores_gemma":[0.8781313,0.0008386232,0.1143412,0.0002506938,0.00003822674,0.0002604619,0.0004723164,0.0000334349,0.005633763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006585958,"threshold_uncertainty_score":0.002733588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008084927242670322,"score_gpt":0.2568633034016152,"score_spread":0.2487783761589449,"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."}}