{"id":"W4313237549","doi":"10.1021/acs.jpcb.2c05660","title":"Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning","year":2022,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"National Research Council Canada; 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; Machine learning; Computational biology; Biology; RNA; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004592571,0.0003261179,0.0003989451,0.0002407064,0.0001979136,0.0002932442,0.0004814423,0.0004370489,0.0003423977],"category_scores_gemma":[0.0007894415,0.0002291874,0.0004793998,0.0001934358,0.0005347747,0.0004409252,0.000378061,0.0005119789,0.0001461523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004327627,"about_ca_system_score_gemma":0.0003530745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003475547,"about_ca_topic_score_gemma":0.0006669342,"domain_scores_codex":[0.9997699,0.00008219522,0.00001030836,0.00005921756,0.00006022443,0.00001834896],"domain_scores_gemma":[0.9996778,0.0001743078,0.00005683012,0.0000481672,0.00002885993,0.00001413098],"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":[0.00008416898,0.000209624,0.001857795,0.0001283656,0.00007946678,0.00008693286,0.0001054788,0.7508196,0.1618603,0.0148786,0.0003563696,0.06953329],"study_design_scores_gemma":[0.00001042384,0.00006744839,0.0001361391,0.000003110527,0.000005328916,0.00002588041,0.000004814499,0.9794087,0.01652338,0.003350497,0.0004572229,0.000007095772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1606928,0.0001745279,0.8364318,0.000114097,0.00001224864,0.00008372504,0.00003749097,0.0004607311,0.001992547],"genre_scores_gemma":[0.78316,0.0001938099,0.2149468,0.0001064721,0.00001303264,0.0002130893,0.00009829904,0.0000524994,0.001215922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0004814423,"threshold_uncertainty_score":0.003139913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02318486299505611,"score_gpt":0.2371130552736206,"score_spread":0.2139281922785645,"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."}}