{"id":"W4401732364","doi":"10.1016/j.bios.2024.116680","title":"Truncations and in silico docking to enhance the analytical response of aptamer-based biosensors","year":2024,"lang":"en","type":"article","venue":"Biosensors and Bioelectronics","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; York University; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Nederlandse Onderzoekschool Voor Astronomie","keywords":"Aptamer; In silico; Biosensor; Oligonucleotide; Docking (animal); Chemistry; Systematic evolution of ligands by exponential enrichment; Combinatorial chemistry; Computational biology; Nanotechnology; Biochemistry; Materials science; Biology; RNA; Molecular biology; DNA","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":[0.0006827794,0.0007994054,0.0008322337,0.0002583727,0.0002222497,0.000599521,0.0005665252,0.0005964462,0.0007550256],"category_scores_gemma":[0.001462354,0.0003244309,0.000444274,0.0002886353,0.0003808631,0.0004144613,0.0005025381,0.001027451,0.0006653145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004620723,"about_ca_system_score_gemma":0.0003459421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007153231,"about_ca_topic_score_gemma":0.001022992,"domain_scores_codex":[0.9994019,0.0001596138,0.00008499643,0.0001046873,0.0001850538,0.00006377928],"domain_scores_gemma":[0.9995251,0.0001730732,0.00009003101,0.00007903425,0.0000854445,0.00004728234],"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":[0.000138609,0.00009643606,0.0004993323,0.00006887533,0.00001886729,0.0001517552,0.00005360344,0.01914459,0.9730206,0.0003254571,0.00009343463,0.006388482],"study_design_scores_gemma":[0.00001173272,0.0001793144,0.0003269327,0.00000561568,0.00001460311,0.0001594941,0.00002512334,0.0395834,0.9583377,0.00009054841,0.001247276,0.00001820105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8586093,0.0004897573,0.1375255,0.0002240804,0.00008229852,0.0001681367,0.0002685623,0.0009111019,0.001721268],"genre_scores_gemma":[0.9099301,0.0005130894,0.08695691,0.0001319076,0.000008904035,0.00007503175,0.0005251993,0.0001764859,0.001682394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008322337,"threshold_uncertainty_score":0.003610969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007811351809587927,"score_gpt":0.3075846569040784,"score_spread":0.2997733050944905,"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."}}