{"id":"W4281786804","doi":"10.1038/s43588-022-00249-6","title":"Generative aptamer discovery using RaptGen","year":2022,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Institute of Genetics; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Autoencoder; In silico; Computer science; Artificial intelligence; Generative model; Computational biology; Bayesian probability; Hidden Markov model; Embedding; Machine learning; Pattern recognition (psychology); Generative grammar; Biology; Deep learning; Genetics; RNA; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002663892,0.00007874594,0.00006402491,0.0000783124,0.0005812586,0.00004436394,0.0002303011,0.00004269739,0.000003335378],"category_scores_gemma":[0.00006202153,0.00007014058,0.0000506252,0.0004828355,0.0002453418,0.00001199616,0.0002604136,0.0001685471,3.486603e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005854834,"about_ca_system_score_gemma":0.0002098398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003750405,"about_ca_topic_score_gemma":0.000001672933,"domain_scores_codex":[0.9989682,0.00003968693,0.00009277636,0.0003489289,0.000403336,0.0001470907],"domain_scores_gemma":[0.9996312,0.00001181925,0.00006484426,0.0001241125,0.0001315015,0.00003650302],"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":[0.00001919094,0.00003310156,0.0003801881,7.686433e-7,0.00001237784,0.000002536873,0.00001591503,0.04986797,0.9469169,0.001319078,0.0003204172,0.001111493],"study_design_scores_gemma":[0.0003570135,0.0003132215,0.00386053,0.000006266577,0.00002909551,0.0001475521,0.0001801461,0.07239684,0.8997704,0.007174928,0.01516785,0.0005961737],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9186176,0.0003117553,0.08016725,0.0003225572,0.0001722897,0.00008940541,0.00003955496,0.00002091333,0.0002586766],"genre_scores_gemma":[0.9491494,0.000003688269,0.04953614,0.001029063,0.0001056917,0.000003449941,0.00007199417,0.00000509322,0.00009546425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04714658,"threshold_uncertainty_score":0.4470629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009713030648766307,"score_gpt":0.3105372633547858,"score_spread":0.3008242327060194,"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."}}