{"id":"W4413465154","doi":"10.1039/d5sd00091b","title":"Evaluation of machine learning and deep learning models for the classification of a single extracellular vesicles spectral library","year":2025,"lang":"en","type":"article","venue":"Sensors & Diagnostics","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Fondation Charles-Bruneau; Canadian Cancer Society Research Institute; Canada Research Chairs; McGill University; McGill University Health Centre; Canadian Cancer Society; Canadian Institutes of Health Research; Université du Québec à Montréal; CMC Microsystems; Fondation Brain Canada; Institut de recherche, Centre universitaire de santé McGill","keywords":"Extracellular vesicles; Artificial intelligence; Deep learning; Computer science; Vesicle; Extracellular; Simple (philosophy); Machine learning; Chemistry; Biology; Cell biology; Biochemistry; Philosophy; Membrane; Epistemology","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":[0.0005724698,0.000115547,0.0001297253,0.00005106882,0.00009645808,0.00001851018,0.0001163234,0.0001001236,0.000007505383],"category_scores_gemma":[0.001821996,0.0001043161,0.00007602551,0.0001034405,0.0001488709,0.00001019428,0.00006771521,0.0001011445,3.069008e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001087314,"about_ca_system_score_gemma":0.00007382406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004354766,"about_ca_topic_score_gemma":0.000005569246,"domain_scores_codex":[0.9989258,0.0002298066,0.0002763726,0.0002255846,0.0002027793,0.0001396629],"domain_scores_gemma":[0.9989327,0.0004471069,0.0001854469,0.0002053308,0.000195574,0.0000338286],"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":[0.00009885441,0.0001480733,0.007454453,0.00009257857,0.0001189098,6.348367e-7,0.0001498252,0.04264893,0.9212292,0.002079135,0.00007290488,0.0259065],"study_design_scores_gemma":[0.0006623831,0.0001950965,0.00495854,0.00005268169,0.0004863733,0.000001539596,0.0004293682,0.4817158,0.5066295,0.003093059,0.001653885,0.000121825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9285756,0.04635105,0.02394522,0.0001600125,0.0000547654,0.0004650652,0.00002001977,0.00001368493,0.0004145391],"genre_scores_gemma":[0.9952496,0.002302993,0.001980649,0.00001326746,0.00005395419,0.00002494926,0.0001243143,0.00002314714,0.0002271508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4390669,"threshold_uncertainty_score":0.4253888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515011902067336,"score_gpt":0.2693082036373832,"score_spread":0.2441580846167099,"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."}}