{"id":"W3197910387","doi":"10.33774/chemrxiv-2021-v9rzc","title":"E2EDNA: Simulation Protocol for DNA Aptamers with Ligands","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Research Council Canada","keywords":"Aptamer; Computer science; Protocol (science); Analyte; DNA; Computational biology; Molecular dynamics; Folding (DSP implementation); Systematic evolution of ligands by exponential enrichment; Chemistry; RNA; Biology; Engineering; Biochemistry; Computational chemistry; 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":[0.0006750398,0.001082212,0.0008782624,0.0004571523,0.0007931446,0.0006407687,0.00209909,0.001404586,0.03538624],"category_scores_gemma":[0.001883367,0.0006309875,0.0007550148,0.0006210618,0.0003651668,0.000728253,0.00110346,0.002241874,0.005507203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006886573,"about_ca_system_score_gemma":0.001335861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004086878,"about_ca_topic_score_gemma":0.00434124,"domain_scores_codex":[0.9997514,0.00007576639,0.000018372,0.00004002601,0.00007110219,0.00004334002],"domain_scores_gemma":[0.9995651,0.0002361233,0.00001585487,0.00005746108,0.00008214884,0.00004336108],"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":[0.000724085,0.0005478968,0.003075027,0.001402926,0.0002947709,0.0008439951,0.0007429799,0.6574392,0.04007016,0.1366106,0.09903923,0.05920912],"study_design_scores_gemma":[0.0003480471,0.0000642236,0.0004709641,0.00007124514,0.00002911898,0.0001381404,0.00005907286,0.8399215,0.01565725,0.03445566,0.108705,0.00007968362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"protocol","genre_scores_codex":[0.08514354,0.001107958,0.7953973,0.001380061,0.0006561542,0.001289266,0.04160967,0.02679333,0.04662272],"genre_scores_gemma":[0.2412326,0.001341148,0.6748723,0.0009050997,0.0001077874,0.01215203,0.02744178,0.02161557,0.02033176],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.03538624,"threshold_uncertainty_score":0.1183788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0228441736276796,"score_gpt":0.34995763395914,"score_spread":0.3271134603314604,"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."}}