{"id":"W4388896421","doi":"10.1021/acs.analchem.3c02881","title":"Apta FastZ: An Algorithm for the Rapid Identification of Aptamers with Defined Binding Affinities","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; Government of Canada; Northwestern University","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Chemistry; Affinities; Binding affinities; Computational biology; Algorithm; Computer science; Molecular biology; Biochemistry; Biology; Gene; RNA","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":[0.001906099,0.002777725,0.001407356,0.001898773,0.0009999279,0.001837498,0.002395038,0.001888531,0.008544747],"category_scores_gemma":[0.00478135,0.001353235,0.00166609,0.001129563,0.001025714,0.00146313,0.00201783,0.002537847,0.004291956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001049492,"about_ca_system_score_gemma":0.002355476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002176341,"about_ca_topic_score_gemma":0.003357588,"domain_scores_codex":[0.9988472,0.000267248,0.0001037168,0.0002429242,0.000449821,0.00008898395],"domain_scores_gemma":[0.9987965,0.0007407428,0.0001353581,0.00009155161,0.0001915003,0.00004435142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001617636,0.000457349,0.004571247,0.001364887,0.0005716797,0.0004661049,0.0003840954,0.199931,0.07873119,0.02840954,0.04758393,0.6359114],"study_design_scores_gemma":[0.0003062268,0.0002509137,0.0005881276,0.00005297266,0.0000689743,0.000379633,0.00004447946,0.9121087,0.03571061,0.01583455,0.03456305,0.00009181531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006588476,0.000633784,0.9730515,0.000201914,0.00008603526,0.0002600035,0.0004811194,0.01727441,0.001422718],"genre_scores_gemma":[0.04302292,0.0005585282,0.9478818,0.0003611252,0.00003848086,0.001135155,0.001559832,0.001431484,0.004010748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008544747,"threshold_uncertainty_score":0.02858502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01960645200320915,"score_gpt":0.2875465642248261,"score_spread":0.267940112221617,"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."}}