{"id":"W4410244304","doi":"10.1039/d4mo00245h","title":"A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children","year":2025,"lang":"en","type":"article","venue":"Molecular Omics","topic":"Microbial Inactivation Methods","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Wageningen University and Research; Danone Nutricia Research; Radboud Universitair Medisch Centrum; Prince of Songkla University; Newcastle upon Tyne Hospitals NHS Foundation Trust; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Universiteit Utrecht; Precursory Research for Embryonic Science and Technology; York University; Mahidol University; Danone; Chulalongkorn University; Texas Children's Hospital","keywords":"Omics; Classifier (UML); Cow's milk allergy; Allergy; Machine learning; Artificial intelligence; Computer science; Food allergy; Medicine; Biology; Bioinformatics; Immunology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002969769,0.0001640728,0.0002287631,0.0001285658,0.00003942554,0.0000131987,0.000216668,0.0002328425,0.000003577351],"category_scores_gemma":[0.0003794608,0.0001848794,0.0001346763,0.0001586152,0.0000572803,0.000002824597,0.0001304639,0.000156559,6.581938e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003529747,"about_ca_system_score_gemma":0.000100863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008533895,"about_ca_topic_score_gemma":0.00006823652,"domain_scores_codex":[0.9989454,0.0001289297,0.0003178068,0.000338398,0.00005655535,0.0002128824],"domain_scores_gemma":[0.9994309,0.00002233084,0.0001254121,0.0002892977,0.0000980344,0.00003404939],"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":[0.0001380976,0.00009048687,0.01053254,0.00002350329,0.0001185299,6.830448e-7,0.00002609123,0.001561113,0.9857855,0.0005041722,0.0001075595,0.001111662],"study_design_scores_gemma":[0.001935067,0.00008734265,0.00346836,0.00001612526,0.00001857525,0.000003086064,0.00002214354,0.005711067,0.9788173,0.0001243331,0.009610621,0.0001859427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6987811,0.0004630579,0.3000082,0.0001069796,0.00007096817,0.0003553076,0.00004050007,0.000008211528,0.00016574],"genre_scores_gemma":[0.890873,0.0001839911,0.1065708,0.000601916,0.00003248353,0.00004230297,0.0005911309,0.00004619725,0.001058256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1934374,"threshold_uncertainty_score":0.7539163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120414734768786,"score_gpt":0.2893750955830766,"score_spread":0.277333622106198,"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."}}