{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001191698,0.0001497992,0.0002135384,0.00003760622,0.00003696043,0.00002310249,0.00007612022,0.0001402551,0.000001111635],"category_scores_gemma":[0.0001814895,0.0001415872,0.00003957155,0.0001375073,0.0001530467,0.000004961928,0.0001912759,0.00006406185,2.520977e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009789022,"about_ca_system_score_gemma":0.00001399429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008384683,"about_ca_topic_score_gemma":0.000001765133,"domain_scores_codex":[0.999168,0.00001407367,0.0001979884,0.0003368846,0.00009039505,0.0001926699],"domain_scores_gemma":[0.9994469,0.00001543388,0.00007913221,0.0002458354,0.00008552259,0.0001271722],"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":[0.00003851258,0.00001885225,0.0004577617,0.00005385634,0.00003514884,0.000001273106,0.00000591162,0.000003095326,0.9983464,0.00002016039,0.000009016757,0.001010009],"study_design_scores_gemma":[0.0002322574,0.0000791448,0.0003475671,0.00005161185,0.00007358166,0.00002099249,0.00002372632,0.01362649,0.9850963,0.00003669425,0.000233716,0.0001779289],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939101,0.00001271512,0.005459297,0.00009807697,0.000006139168,0.00005585705,0.00002131162,0.00001537385,0.0004211505],"genre_scores_gemma":[0.9917156,0.00003159722,0.007766578,0.0001292272,0.00006067351,0.000001053204,0.00007768563,0.00001552125,0.000202094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0136234,"threshold_uncertainty_score":0.5773759,"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."}}