{"id":"W4290856461","doi":"10.48550/arxiv.2208.05341","title":"Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universities Space Research Association; Natural Sciences and Engineering Research Council of Canada; McGill University; Canadian Institute for Advanced Research","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Computer science; Artificial intelligence; Computational biology; Machine learning; RNA; Biology; Genetics","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":[0.0004316439,0.0003206836,0.0003398445,0.000228213,0.0001937844,0.0003065223,0.0004204744,0.0003981584,0.0003565638],"category_scores_gemma":[0.0007349805,0.0002161688,0.000420627,0.0001736016,0.0005235401,0.0004577872,0.0003621553,0.0004781293,0.0001482207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004433019,"about_ca_system_score_gemma":0.0003303514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003340693,"about_ca_topic_score_gemma":0.00066191,"domain_scores_codex":[0.9997984,0.00006981,0.00000922831,0.00005484134,0.0000532695,0.00001453175],"domain_scores_gemma":[0.9997378,0.0001419455,0.00004712811,0.00003812553,0.00002263061,0.00001239825],"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":[0.00008021959,0.0002114541,0.001746434,0.0001298142,0.00006643079,0.0000900235,0.0001009588,0.6955218,0.2171393,0.01756483,0.000377509,0.06697132],"study_design_scores_gemma":[0.00001125278,0.00007018034,0.0001550796,0.000003531523,0.000005711579,0.00002980037,0.000005029791,0.9713082,0.02382718,0.003997724,0.0005785226,0.000007836529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.163899,0.0002134073,0.832947,0.0001287677,0.00001384669,0.00009066713,0.00004341496,0.0004887887,0.002175059],"genre_scores_gemma":[0.7718883,0.0002419605,0.2257779,0.000109802,0.00001338179,0.0002213536,0.0001006495,0.0000564776,0.001590276],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0004433019,"threshold_uncertainty_score":0.003216326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09541077361623018,"score_gpt":0.1998018117495725,"score_spread":0.1043910381333423,"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."}}