{"id":"W2312438577","doi":"10.1021/acscombsci.5b00163","title":"Screening and Identification of DNA Aptamers to Tyramine Using <i>in Vitro</i> Selection and High-Throughput Sequencing","year":2016,"lang":"en","type":"article","venue":"ACS Combinatorial Science","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministero dell'Economia e delle Finanze; Consiglio Nazionale delle Ricerche","keywords":"Aptamer; Microscale thermophoresis; Systematic evolution of ligands by exponential enrichment; Chemistry; Computational biology; DNA; DNA sequencing; Tyramine; Selection (genetic algorithm); RNA; Combinatorial chemistry; Biochemistry; Molecular biology; Biology; Computer science; Gene; Machine learning","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":[0.0005268493,0.0006401855,0.000655072,0.0004239452,0.0002138633,0.000647797,0.0003766539,0.0004824407,0.0006180321],"category_scores_gemma":[0.0008554396,0.0002709311,0.0006519896,0.0004628029,0.0002402674,0.000212919,0.0004254813,0.000440652,0.0007342524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002831611,"about_ca_system_score_gemma":0.0002759279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004611623,"about_ca_topic_score_gemma":0.0007585627,"domain_scores_codex":[0.9992513,0.0002232442,0.0000662827,0.0001635751,0.0002078669,0.00008775189],"domain_scores_gemma":[0.9995937,0.0001525193,0.00008816033,0.00004625201,0.00008085773,0.00003846623],"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":[0.00003333976,0.00002524211,0.0002376636,0.00003867244,0.000007746609,0.0000414996,0.00002127645,0.0003926892,0.9961736,0.00005544545,0.00003313623,0.002939675],"study_design_scores_gemma":[0.00000350512,0.0001247411,0.0007334407,0.000002497373,0.00001321116,0.0001038176,0.00001324531,0.001780158,0.9961557,0.00003183385,0.001032329,0.000005483162],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7789257,0.001256332,0.2132172,0.0001584885,0.00005300456,0.0006398809,0.001406907,0.0007475172,0.003595007],"genre_scores_gemma":[0.8289735,0.002080329,0.16004,0.0002396944,0.00002364912,0.0005583967,0.002403515,0.0002084941,0.005472425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000655072,"threshold_uncertainty_score":0.002786279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01140208472014839,"score_gpt":0.27270255038865,"score_spread":0.2613004656685016,"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."}}