{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000466737,0.00007200255,0.00009802501,0.00009136923,0.00009235803,0.00002201011,0.00008408329,0.00005059747,6.875317e-8],"category_scores_gemma":[0.0001979782,0.00005670633,0.00001194501,0.0003800776,0.0002334054,0.00002259315,0.00009780122,0.00002593452,6.637852e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004186408,"about_ca_system_score_gemma":0.0000440182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009768605,"about_ca_topic_score_gemma":0.00001098766,"domain_scores_codex":[0.9991997,0.0000237316,0.0001673573,0.0003216827,0.000138511,0.000148955],"domain_scores_gemma":[0.9996276,0.00001229598,0.00009157203,0.0001205894,0.00009920207,0.00004876782],"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":[0.0000315397,0.000006750778,0.000467623,0.000002550814,0.000002617611,2.092848e-7,0.00001441719,0.000003705283,0.9911507,0.000345376,0.000001602287,0.007972876],"study_design_scores_gemma":[0.0002110344,0.00008343701,0.0007932056,0.0000221049,0.000006964885,0.000004669241,0.00002801155,0.0001890757,0.997929,0.0006138422,0.00003299165,0.00008570818],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885735,0.00001932795,0.01112373,0.00007837795,0.0001053326,0.00008030012,0.000002903287,0.000009003388,0.000007558962],"genre_scores_gemma":[0.9937069,0.00003390893,0.006170253,0.00002484608,0.00004946878,0.000001452623,0.000001290889,0.000004501803,0.000007394132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007887167,"threshold_uncertainty_score":0.2312417,"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."}}