{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001403801,0.0002770535,0.0003230673,0.0001226754,0.000182359,0.0002273345,0.0003430977,0.0004821139,0.001566704],"category_scores_gemma":[0.0004454837,0.0001639727,0.0002475539,0.0001156358,0.0002003788,0.0001761496,0.0001632144,0.0003007158,0.0005087662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004001889,"about_ca_system_score_gemma":0.0004704857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006072932,"about_ca_topic_score_gemma":0.0009510326,"domain_scores_codex":[0.9999011,0.00001281822,0.000005501595,0.00002985387,0.00003489856,0.0000158496],"domain_scores_gemma":[0.9999032,0.00003844103,0.00001827148,0.00000932778,0.00001703193,0.00001377952],"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":[0.0002676775,0.0001320581,0.0007733534,0.0002002418,0.00002833785,0.000433922,0.0000455035,0.06728183,0.9109877,0.00384384,0.0003062282,0.01569927],"study_design_scores_gemma":[0.0001143616,0.0008221702,0.0007370031,0.00001295549,0.00004519268,0.0003846026,0.00003445817,0.2423656,0.7492571,0.000861657,0.00533085,0.00003410548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8973796,0.0005436545,0.09230801,0.0002110593,0.0001134141,0.0002436073,0.0002622541,0.0005794251,0.008358895],"genre_scores_gemma":[0.9291489,0.0002239023,0.06680863,0.00009552426,0.000006934407,0.0001939123,0.000253845,0.00004852318,0.00321983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001566704,"threshold_uncertainty_score":0.005241156,"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."}}