{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002496838,0.0001624479,0.000178096,0.0001374691,0.0002182983,0.00001049709,0.0001558956,0.0001510853,0.000003694605],"category_scores_gemma":[0.0002033592,0.0001505418,0.0001272283,0.0002057122,0.0001490137,0.000003835865,0.00008160172,0.00006492827,0.00001635291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003574281,"about_ca_system_score_gemma":0.0001363447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006275449,"about_ca_topic_score_gemma":0.000002898987,"domain_scores_codex":[0.9987949,0.0001169455,0.0002451679,0.000473771,0.00009452502,0.0002747205],"domain_scores_gemma":[0.9992027,0.000242774,0.00009981473,0.0001647998,0.0001834386,0.0001064895],"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":[0.0003015746,0.0001200061,0.0004600168,0.0000366553,0.0002972979,0.00000434086,0.0000558214,0.8501677,0.1285753,0.003454125,0.0146186,0.001908521],"study_design_scores_gemma":[0.0004979682,0.000239092,0.00004921685,0.000006917604,0.00004920661,0.00001545295,0.00005608947,0.8980722,0.02183659,0.05903818,0.01979354,0.0003454951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02883238,0.0002409035,0.9691108,0.001116996,0.00006242011,0.0003142617,0.00009565035,0.0001793663,0.00004721906],"genre_scores_gemma":[0.7761492,0.00009955219,0.2193394,0.001105715,0.000213739,0.00007365814,0.002782977,0.00002163019,0.000214193],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7497714,"threshold_uncertainty_score":0.6138917,"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."}}