{"id":"W2074541406","doi":"10.1021/ac201242r","title":"Protein Labeling Enhances Aptamer Selection by Methods of Kinetic Capillary Electrophoresis","year":2011,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Aptamer; Capillary electrophoresis; Chemistry; Electrophoresis; DNA; Selection (genetic algorithm); Protein detection; Chromatography; Gel electrophoresis; Computational biology; Biochemistry; Molecular biology; Nanotechnology; Biology; Machine learning; Computer science","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":[0.0008035318,0.0004652011,0.0003675792,0.0004310984,0.0002021616,0.000568706,0.0005746413,0.0004947839,0.0007408239],"category_scores_gemma":[0.001084679,0.0002823715,0.0002148105,0.0003400634,0.0005733201,0.0004823982,0.0003666283,0.0007083785,0.0004333565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003505654,"about_ca_system_score_gemma":0.0002465246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004237987,"about_ca_topic_score_gemma":0.0004644454,"domain_scores_codex":[0.9991859,0.0002228997,0.00006688412,0.0001868662,0.0002595101,0.0000778557],"domain_scores_gemma":[0.9993721,0.0003224209,0.00009392976,0.00006093275,0.0001055937,0.00004513957],"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":[0.00001720676,0.000009503001,0.00005304015,0.00002930342,0.000001490955,0.00001548236,0.00001000217,0.0000598516,0.9978404,0.0002475521,0.00003539407,0.001680559],"study_design_scores_gemma":[0.000004382359,0.00002923517,0.0003270783,0.0000027896,0.000003185614,0.0001348204,0.000003021347,0.001838116,0.9962759,0.00005967245,0.001315865,0.000005930903],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5713264,0.003208211,0.4185281,0.0005033494,0.0001201173,0.0003039678,0.000164938,0.001359929,0.004484854],"genre_scores_gemma":[0.7632483,0.002265788,0.2294029,0.0002515614,0.00004645414,0.0002638935,0.0002583324,0.0002119234,0.004050775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008035318,"threshold_uncertainty_score":0.004249513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01253157490657162,"score_gpt":0.2955577100844351,"score_spread":0.2830261351778635,"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."}}