{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003782048,0.001345393,0.0009874084,0.002213935,0.0005157679,0.0008144559,0.0007068008,0.0009268156,0.0008470708],"category_scores_gemma":[0.00313888,0.0003826164,0.0007452382,0.001220688,0.0004144113,0.0006384933,0.0009279131,0.0005933902,0.0006835219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007455344,"about_ca_system_score_gemma":0.0008190099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001318829,"about_ca_topic_score_gemma":0.00259934,"domain_scores_codex":[0.995028,0.0009725959,0.0005340516,0.0006416572,0.002481927,0.0003418416],"domain_scores_gemma":[0.998555,0.0004859094,0.0001342525,0.000153964,0.0006159834,0.00005483335],"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":[0.00009075223,0.0000750734,0.0005968721,0.000210604,0.0000630076,0.00003717623,0.00005816624,0.0006467823,0.9755814,0.0001635451,0.000148367,0.02232832],"study_design_scores_gemma":[0.0000049887,0.0003271741,0.001837109,0.00001494792,0.00005882333,0.0001588633,0.0000313081,0.001755258,0.9933017,0.00007880355,0.002410742,0.00002028235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.63526,0.02702532,0.3214935,0.0003657564,0.0001991195,0.002202814,0.001998296,0.001716883,0.009738293],"genre_scores_gemma":[0.6873012,0.02009936,0.2827436,0.0005016961,0.00006659067,0.001232619,0.003086138,0.0002824171,0.004686374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003782048,"threshold_uncertainty_score":0.02000165,"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."}}