{"id":"W2785704273","doi":"10.22215/etd/2017-12144","title":"The Selection of DNA Aptamers for the Prevention of Alpha-Synuclein Aggregation as a Therapeutic Tool in Parkinson's Disease","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Oligonucleotide; Computational biology; Alpha-synuclein; Selection (genetic algorithm); DNA; Biology; Chemistry; Parkinson's disease; Disease; Medicine; Molecular biology; Computer science; Genetics; RNA; Gene; Pathology; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003947455,0.0004064171,0.0004641644,0.0003711855,0.0001931185,0.0004503162,0.0002878318,0.000576894,0.0006316659],"category_scores_gemma":[0.0003645854,0.0002730732,0.0003473313,0.000279505,0.0002579041,0.0002754511,0.000320185,0.0006145563,0.000376542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003001676,"about_ca_system_score_gemma":0.0002033535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000157907,"about_ca_topic_score_gemma":0.0002914834,"domain_scores_codex":[0.9997537,0.00005460338,0.00002313413,0.00006126145,0.00008247522,0.00002491609],"domain_scores_gemma":[0.9998885,0.00003349449,0.00002279304,0.0000103868,0.00002326188,0.0000215547],"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":[0.0000850912,0.00007109121,0.0001450571,0.00009245967,0.00001789157,0.0001040856,0.00003445647,0.0009110555,0.986604,0.0003074949,0.0001563955,0.01147102],"study_design_scores_gemma":[0.00004636379,0.000739879,0.0007662445,0.00002220383,0.00004640342,0.0004665255,0.00002152995,0.004049193,0.9861058,0.0003309714,0.007385727,0.00001920316],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8978058,0.02294245,0.06977929,0.001009094,0.0002258317,0.0004695902,0.0004583875,0.000508685,0.006800761],"genre_scores_gemma":[0.9471713,0.009203326,0.03600791,0.000618555,0.00004517979,0.0002740417,0.0005535324,0.00006245379,0.006063679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006316659,"threshold_uncertainty_score":0.002177894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01124397925013329,"score_gpt":0.309298887677614,"score_spread":0.2980549084274807,"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."}}