{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009308086,0.0005547104,0.0006326323,0.0005409886,0.0002997626,0.0006169674,0.0009627935,0.001036905,0.001588492],"category_scores_gemma":[0.002101417,0.0007260104,0.0009575083,0.0003281153,0.0008532628,0.0007685584,0.001022684,0.0009938532,0.0004230907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007108495,"about_ca_system_score_gemma":0.0007022316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001443983,"about_ca_topic_score_gemma":0.002024651,"domain_scores_codex":[0.9995688,0.0001525038,0.00002028685,0.0001022481,0.0001145079,0.00004171116],"domain_scores_gemma":[0.9992366,0.0004782545,0.00006610775,0.00009495723,0.00008952143,0.00003457961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003237158,0.00003031369,0.0004756243,0.00005023735,0.00004534309,0.00007860777,0.00005133387,0.9411783,0.007102112,0.02164659,0.0004204541,0.02888876],"study_design_scores_gemma":[0.000002499984,0.000006670301,0.00001655748,0.000001626734,0.000002473162,0.00001354222,0.000001256724,0.9957151,0.001295689,0.002705711,0.0002358999,0.000003006488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01524107,0.00008472686,0.9824281,0.00008928318,0.0000136732,0.00002773535,0.00004357444,0.0005655817,0.001506235],"genre_scores_gemma":[0.5381507,0.0002104432,0.4557612,0.0002949982,0.00002481211,0.0002377876,0.0003713597,0.0003291605,0.004619529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001588492,"threshold_uncertainty_score":0.005314052,"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."}}