{"id":"W4313454382","doi":"10.1021/acssynbio.2c00462","title":"Machine Learning Directed Aptamer Search from Conserved Primary Sequences and Secondary Structures","year":2023,"lang":"en","type":"article","venue":"ACS Synthetic Biology","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Waterloo; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aptamer; Computational biology; Sequence (biology); Systematic evolution of ligands by exponential enrichment; Sequence analysis; Protein secondary structure; Biology; Conserved sequence; Selection (genetic algorithm); Computer science; Artificial intelligence; Genetics; Base sequence; Gene; Biochemistry; RNA","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":[0.0002294248,0.0001962492,0.0002676011,0.00009432463,0.0001552298,0.00002139481,0.0001599062,0.0002808182,0.00001748594],"category_scores_gemma":[0.0001494226,0.000154823,0.00006375051,0.0001712915,0.0004971623,0.000003364337,0.000239821,0.0002325174,0.000007304895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009587756,"about_ca_system_score_gemma":0.00005071548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002700385,"about_ca_topic_score_gemma":0.0000647319,"domain_scores_codex":[0.9986037,0.0002714758,0.0002043043,0.0005406095,0.00007349874,0.0003064351],"domain_scores_gemma":[0.9994197,0.0001082957,0.00007529594,0.0002665088,0.0000615182,0.00006871786],"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":[0.00005059172,0.000008775991,0.006980666,0.000009429665,0.0001046119,0.00000718906,0.00002847842,0.000002889501,0.9633379,0.00006660852,0.0001450706,0.02925781],"study_design_scores_gemma":[0.0003485416,0.0003175939,0.01117622,0.000020618,0.0000574349,0.00004309578,0.0001277043,0.0003401733,0.9611334,0.002879747,0.02315992,0.0003955744],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966145,0.001745223,0.0001581936,0.0004597731,0.00006706034,0.0001004044,0.0001260464,0.0002227591,0.0005060302],"genre_scores_gemma":[0.9934626,0.002079291,0.00243626,0.0003169225,0.00009423034,0.000007496731,0.00119057,0.00002188947,0.0003907974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02886223,"threshold_uncertainty_score":0.63135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01412388952715324,"score_gpt":0.2717016576565557,"score_spread":0.2575777681294025,"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."}}