{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005107418,0.0001778115,0.0001903811,0.0001869015,0.00007843295,0.00004686488,0.000100798,0.0001382227,8.110173e-7],"category_scores_gemma":[0.0001960156,0.0001229437,0.0000780901,0.0005134132,0.0002422814,0.000004271716,0.00006042295,0.0001405623,7.882231e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002612243,"about_ca_system_score_gemma":0.000100504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002133314,"about_ca_topic_score_gemma":0.0001247587,"domain_scores_codex":[0.9987562,0.0001259773,0.0002710079,0.0004440744,0.0001189158,0.0002838127],"domain_scores_gemma":[0.9994261,0.0001135937,0.00004702022,0.0002854396,0.00005747505,0.00007032142],"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":[0.0003808035,0.0000378748,0.0003701369,0.00002365397,0.00003774071,0.000004220651,0.0000462396,0.00004771021,0.9906397,0.0002762571,0.0001736001,0.007962056],"study_design_scores_gemma":[0.0001066252,0.0005876712,0.001485588,0.00007088904,0.00004655954,0.00001358615,0.0001082058,0.007131962,0.9762599,0.00006938801,0.01389442,0.0002251677],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941577,0.002247215,0.0003782953,0.00293614,0.00002383561,0.0001773232,0.00002356857,0.0000281818,0.00002776575],"genre_scores_gemma":[0.9977098,0.0007217351,0.001050839,0.000288287,0.00004605545,0.000007025777,0.00001267289,0.00001681411,0.000146776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01437977,"threshold_uncertainty_score":0.50135,"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."}}