{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004724835,0.0001111099,0.0001852713,0.00006999862,0.00007629921,0.00002829031,0.00004911797,0.0001263558,0.000001931857],"category_scores_gemma":[0.0002005052,0.00009653159,0.00008297073,0.0001974956,0.0001033314,0.000005233135,0.00004291029,0.00005014338,6.016134e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009259012,"about_ca_system_score_gemma":0.00004950403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003569487,"about_ca_topic_score_gemma":0.000007903099,"domain_scores_codex":[0.9988144,0.00001962075,0.0002596358,0.0005803379,0.0001383774,0.0001876098],"domain_scores_gemma":[0.9991653,0.00001545597,0.000220282,0.0002893762,0.0002584407,0.00005112199],"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":[0.00004300408,0.00003539578,0.0009500441,0.00002406111,0.00002715369,8.257717e-7,0.000004535291,8.955938e-7,0.9978954,0.00002838925,0.0003935873,0.0005967503],"study_design_scores_gemma":[0.000121917,0.000178879,0.0002032243,0.00001127173,0.00002908368,0.0000708523,0.00000933405,0.00007352639,0.9943499,0.0006612555,0.004167865,0.0001228921],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985237,0.0001276337,0.0006646083,0.00003569041,0.0002888844,0.0002659033,0.000004317591,0.00002210018,0.00006716953],"genre_scores_gemma":[0.9921183,0.00002503036,0.006564524,0.00001743951,0.00005262232,0.000006664297,0.00009673869,0.00001083369,0.001107849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006405395,"threshold_uncertainty_score":0.3936444,"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."}}