{"id":"W2071398114","doi":"10.1089/nat.2013.0415","title":"A Computationally Designed DNA Aptamer Template with Specific Binding to Phosphatidylserine","year":2013,"lang":"en","type":"article","venue":"Nucleic Acid Therapeutics","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Cancer Foundation; Allard Foundation; King Abdulaziz City for Science and Technology; Ministry of Advanced Education, Government of Alberta; Compute Canada","keywords":"Aptamer; Phosphatidylserine; Nucleic acid; DNA; Oligonucleotide; Computational biology; In silico; SELEX Aptamer Technique; Biology; Chemistry; Nanotechnology; Biophysics; Cell biology; Biochemistry; Molecular biology; RNA; Phospholipid; Membrane; Systematic evolution of ligands by exponential enrichment; Materials science; Gene","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":[0.00008518173,0.0002136358,0.0001732931,0.0000686747,0.0001203069,0.00006544744,0.0001811568,0.0001058275,0.00002544921],"category_scores_gemma":[0.000004731283,0.000166582,0.00006431005,0.0002311668,0.000086903,0.000008589966,0.00005877713,0.00007732742,0.00007203955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002200005,"about_ca_system_score_gemma":0.00002571184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000501538,"about_ca_topic_score_gemma":0.00000565242,"domain_scores_codex":[0.998953,0.0000338265,0.0002017944,0.0003604386,0.0001804633,0.0002705207],"domain_scores_gemma":[0.9993029,0.00001054123,0.00009229235,0.0003185457,0.0001793495,0.00009633414],"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":[0.0000761306,0.00004594271,0.0003207935,0.000003465736,0.0001142152,0.000002310499,0.00003988371,0.00003121958,0.980508,0.00005022301,0.0005673424,0.01824051],"study_design_scores_gemma":[0.000352092,0.0005102585,0.001607828,0.00002053337,0.00003186195,0.00002223032,0.00008842716,0.0001612487,0.9616756,0.0001287098,0.03506536,0.0003357976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9522568,0.0001264981,0.04613922,0.0005738297,0.00003650188,0.0003293337,0.000009431992,0.00008492203,0.0004434351],"genre_scores_gemma":[0.9167775,0.00009831119,0.08095366,0.001606506,0.00009516761,0.00001728044,0.00006478745,0.00004298974,0.0003438373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03547936,"threshold_uncertainty_score":0.6793019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01481974422973486,"score_gpt":0.2515893620837611,"score_spread":0.2367696178540262,"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."}}