{"id":"W4383216587","doi":"10.1371/journal.pcbi.1010774","title":"DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design","year":2023,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Brock University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Brock University; Howard Hughes Medical Institute","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Computational biology; Drug discovery; SELEX Aptamer Technique; Oligonucleotide; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Binding affinities; Biology; RNA; DNA; Bioinformatics; Genetics; Gene; Medicine","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":[0.0005341909,0.0008824794,0.0008262431,0.0004838253,0.0003492263,0.0006932229,0.001132531,0.001164505,0.003307845],"category_scores_gemma":[0.001352053,0.0004413775,0.0009052382,0.0003280925,0.000345127,0.0003835815,0.0006898571,0.00111615,0.0003984878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005946176,"about_ca_system_score_gemma":0.001211242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003337211,"about_ca_topic_score_gemma":0.00467222,"domain_scores_codex":[0.9998585,0.00005676597,0.000006017916,0.00002071435,0.00003373391,0.00002429975],"domain_scores_gemma":[0.9996784,0.0002219881,0.00001905397,0.000014974,0.00003475529,0.00003084561],"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":[0.00007646879,0.00004554639,0.001428082,0.0001178405,0.00007493705,0.0001015862,0.00003936921,0.9683207,0.00231242,0.007816731,0.001779055,0.0178873],"study_design_scores_gemma":[0.00001814351,0.00001554418,0.00004632384,0.000004768311,0.000005594085,0.000011337,0.000005108533,0.9964708,0.0004244332,0.001865399,0.001129708,0.000002777732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1355638,0.001168184,0.8403485,0.001077763,0.0002300043,0.0002493888,0.001576263,0.003659809,0.01612633],"genre_scores_gemma":[0.5905821,0.0007615898,0.3991393,0.0005959243,0.00007622368,0.0008682093,0.002310023,0.0006068991,0.005059765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003337211,"threshold_uncertainty_score":0.01106584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05978447841760824,"score_gpt":0.3251995578931666,"score_spread":0.2654150794755584,"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."}}