{"id":"W4392660940","doi":"10.1002/anie.202400413","title":"High‐Precision Viral Detection Using Electrochemical Kinetic Profiling of Aptamer‐Antigen Recognition in Clinical Samples and Machine Learning","year":2024,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University Medical Centre; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"False positive paradox; Aptamer; True positive rate; Coronavirus disease 2019 (COVID-19); Computer science; Profiling (computer programming); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); False positives and false negatives; Artificial intelligence; Gold standard (test); Computational biology; Machine learning; Virology; Pattern recognition (psychology); Biology; Medicine; Mathematics; Infectious disease (medical specialty); Molecular biology; Pathology; Statistics","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":[0.0003085434,0.0001145484,0.0001423979,0.0001331192,0.00002956885,0.00002644442,0.00005751294,0.0001679601,0.000004536488],"category_scores_gemma":[0.000266735,0.0001096035,0.00008625558,0.0001101301,0.00007354848,0.00001949831,0.00004973433,0.0002011983,5.951587e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003799583,"about_ca_system_score_gemma":0.00001990779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005481887,"about_ca_topic_score_gemma":0.00002459499,"domain_scores_codex":[0.9990024,0.00004347713,0.0003617459,0.000333275,0.0001487529,0.0001103298],"domain_scores_gemma":[0.9996354,0.00004624768,0.0001058883,0.0000707302,0.0001131022,0.00002864507],"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":[0.0001163065,0.00003303463,0.001351768,0.00001897507,0.00004292695,0.000002281456,0.000007170327,0.000008491453,0.9800503,0.00000862786,0.00001019883,0.01834995],"study_design_scores_gemma":[0.0002100221,0.0001410289,0.0006244541,0.0001326715,0.00002765695,0.00002769792,0.00001694578,0.003251616,0.9942059,0.0007646982,0.0004766728,0.0001206155],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9495134,0.0003975119,0.04962199,0.00004764452,0.0002371142,0.0000770349,0.00002939897,0.00003211744,0.00004381192],"genre_scores_gemma":[0.988547,0.00123822,0.008416829,0.00002603325,0.0007025071,0.000005789553,0.001034438,0.00001472519,0.00001442966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04120516,"threshold_uncertainty_score":0.44695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02937088581218443,"score_gpt":0.3176296475776538,"score_spread":0.2882587617654693,"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."}}