{"id":"W2315963391","doi":"10.1021/ac5001527","title":"Kinetic and Equilibrium Binding Characterization of Aptamers to Small Molecules using a Label-Free, Sensitive, and Scalable Platform","year":2014,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Complementary and Integrative Health; Bill and Melinda Gates Foundation; Natural Sciences and Engineering Research Council of Canada; Advanced Research Projects Agency; National Institutes of Health; National Science Foundation","keywords":"Aptamer; Chemistry; Surface plasmon resonance; Small molecule; Characterization (materials science); Nucleic acid; Systematic evolution of ligands by exponential enrichment; Receptor–ligand kinetics; Binding affinities; Molecular binding; Oligonucleotide; Computational biology; Nanotechnology; Combinatorial chemistry; DNA; RNA; Molecule; Biochemistry; Gene; Molecular biology; Nanoparticle; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001056297,0.0006125167,0.0005038917,0.0005650597,0.0002908097,0.0004231815,0.0006135228,0.000467318,0.0008148343],"category_scores_gemma":[0.001659066,0.0003909887,0.0002853756,0.0003422962,0.0003785358,0.000498999,0.0002831568,0.0008624688,0.0006067568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006192204,"about_ca_system_score_gemma":0.0005214703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009326845,"about_ca_topic_score_gemma":0.001770922,"domain_scores_codex":[0.9987612,0.0002092596,0.0001015887,0.0002580915,0.000555956,0.0001140127],"domain_scores_gemma":[0.9992079,0.0003308093,0.0001061962,0.00008115835,0.0002133579,0.00006059646],"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":[0.00001615511,0.00002216981,0.0001241065,0.00002266776,0.000003090997,0.000007945984,0.00001192866,0.0004955792,0.9975794,0.00007449083,0.00002478042,0.00161775],"study_design_scores_gemma":[0.000004480897,0.00007726611,0.0004734159,0.000001545447,0.000003767605,0.00003071845,0.00000762235,0.005720799,0.9932366,0.00004167149,0.0003940562,0.000007954264],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6764357,0.001883316,0.3170632,0.0001865742,0.00006716664,0.0004779163,0.0006266357,0.0007451809,0.002514242],"genre_scores_gemma":[0.8691881,0.00093785,0.1256526,0.0001474109,0.00002353961,0.0004450435,0.0006511435,0.0001067162,0.002847639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001056297,"threshold_uncertainty_score":0.005586326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437154178468552,"score_gpt":0.2534989887309738,"score_spread":0.2391274469462883,"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."}}