{"id":"W2402540481","doi":"10.1021/acs.analchem.5b02102","title":"Comprehensive Analytical Comparison of Strategies Used for Small Molecule Aptamer Evaluation","year":2015,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":181,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Western Grains Research Foundation","keywords":"Aptamer; Chemistry; Small molecule; Computational biology; Characterization (materials science); Systematic evolution of ligands by exponential enrichment; Nanotechnology; Workflow; Nucleic acid; Combinatorial chemistry; Biochemical engineering; Computer science; Molecular biology; Biochemistry; RNA; Database; Biology","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":[0.0002697124,0.0002024998,0.0003797238,0.00003067566,0.00003727843,0.00002743878,0.0001780471,0.0002460718,0.000006280018],"category_scores_gemma":[0.0003056026,0.0001831087,0.0002277519,0.0001593989,0.0002488101,0.000005119835,0.00007236184,0.000107981,0.000001382219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003543359,"about_ca_system_score_gemma":0.0002079881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009214028,"about_ca_topic_score_gemma":0.000005820512,"domain_scores_codex":[0.9985695,0.00004441043,0.0004102228,0.0004145888,0.0003117392,0.0002495481],"domain_scores_gemma":[0.9984062,0.00004227175,0.0001567828,0.0003948593,0.0008344779,0.0001654345],"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":[0.0002297389,0.0002941222,0.001485763,0.0000949258,0.0003018834,0.000001485942,0.00003750704,0.0004411959,0.9924395,0.000196093,0.003487528,0.0009902382],"study_design_scores_gemma":[0.0007253363,0.0002349422,0.00007530417,0.00001678008,0.0003168049,0.000004121104,0.0008896429,0.05492561,0.9385219,0.0004035539,0.003634527,0.0002515333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799395,0.0002836498,0.01646619,0.0001500016,0.00002194497,0.0002147819,0.0000291666,0.00003069617,0.002864051],"genre_scores_gemma":[0.995689,0.000007995453,0.003393249,0.00005964863,0.0001360736,0.00001486602,0.0004105663,0.00001845999,0.0002701936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05448441,"threshold_uncertainty_score":0.7466955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0847055575142071,"score_gpt":0.3910965053732862,"score_spread":0.3063909478590791,"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."}}