{"id":"W4377087378","doi":"10.1093/nar/gkad424","title":"Selection of allosteric dnazymes that can sense phenylalanine by expression-SELEX","year":2023,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City; McMaster University","keywords":"Aptamer; Deoxyribozyme; Systematic evolution of ligands by exponential enrichment; Biology; DNA; Phenylalanine; Biosensor; Allosteric regulation; SELEX Aptamer Technique; Computational biology; Selection (genetic algorithm); Ligand (biochemistry); Biochemistry; Molecular biology; RNA; Enzyme; Computer science; Receptor; Gene; Amino acid; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008039664,0.001033287,0.0007777135,0.0003535908,0.0001837059,0.0007847284,0.0005363523,0.0004382668,0.0006261333],"category_scores_gemma":[0.0008320576,0.0002617378,0.0003865252,0.0004260119,0.0003462477,0.0003168007,0.0006900892,0.0007333705,0.0005179089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002574079,"about_ca_system_score_gemma":0.0002181653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002033387,"about_ca_topic_score_gemma":0.0003821412,"domain_scores_codex":[0.9995103,0.0001354613,0.00006558161,0.0001037817,0.0001282865,0.00005655867],"domain_scores_gemma":[0.9997135,0.0001199586,0.00006281783,0.00003463527,0.00004285933,0.00002616557],"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":[0.00005312674,0.00005345434,0.0002786067,0.00007284892,0.00001730017,0.0001710668,0.00004755597,0.0005954463,0.9923255,0.0002631271,0.00006591276,0.006056059],"study_design_scores_gemma":[0.00000940717,0.00008440309,0.0003093024,0.000004272067,0.00001586808,0.0001515068,0.00001756263,0.001638096,0.9959059,0.00004733649,0.001808325,0.000007990281],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8283045,0.001230315,0.1650804,0.0002811136,0.00006753322,0.0006331191,0.0006374905,0.0006464818,0.003118925],"genre_scores_gemma":[0.866125,0.002202045,0.1210706,0.0002052094,0.00001713561,0.0005254994,0.001468855,0.0002764783,0.008109215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001033287,"threshold_uncertainty_score":0.004251838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03449183829876351,"score_gpt":0.3447207551161475,"score_spread":0.310228916817384,"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."}}