{"id":"W4281626337","doi":"10.21105/joss.04182","title":"E2EDNA 2.0: Python Pipeline for Simulating DNA Aptamerswith Ligands","year":2022,"lang":"en","type":"article","venue":"The Journal of Open Source Software","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Python (programming language); Aptamer; DNA; Computational biology; Computer science; Biophysics; Programming language; Chemistry; Nanotechnology; Combinatorial chemistry; Materials science; Biology; Molecular biology; Biochemistry","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":[0.001358685,0.0001357446,0.0002394722,0.00005042357,0.0004884887,0.00005112028,0.0007591849,0.0000468286,0.000008193329],"category_scores_gemma":[0.0003077916,0.0000904693,0.0001839546,0.0001521764,0.00006243034,0.000009002893,0.0005543767,0.0002117945,2.824887e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000277551,"about_ca_system_score_gemma":0.00007336683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009327954,"about_ca_topic_score_gemma":0.000004804947,"domain_scores_codex":[0.998968,0.000107303,0.0003669483,0.0001474514,0.0002207343,0.0001895521],"domain_scores_gemma":[0.9988116,0.00008988639,0.000532424,0.0002994748,0.0002097086,0.00005691165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003279459,0.0002928555,0.001842875,0.00003530814,0.0004845648,0.00001410266,0.0009875651,0.01867586,0.7887565,0.00001241361,0.03793116,0.1476873],"study_design_scores_gemma":[0.002168208,0.002281978,0.0001014734,0.00006867362,0.0004226973,0.0005309472,0.003851038,0.001894577,0.6491927,0.0007239524,0.3382349,0.0005288914],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7797458,0.0006444152,0.2178134,0.001010693,0.0001354629,0.0004513967,0.00004796424,0.00003208646,0.0001188118],"genre_scores_gemma":[0.9847859,0.00004845427,0.01220731,0.0007193084,0.000303892,0.00000556783,0.00003280192,0.00003667934,0.001860044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3003037,"threshold_uncertainty_score":0.375711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0180231520955433,"score_gpt":0.3072297345868629,"score_spread":0.2892065824913196,"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."}}