{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001906356,0.0008964972,0.0008911339,0.001965694,0.0005194727,0.001158601,0.000596089,0.001219318,0.001107224],"category_scores_gemma":[0.003402711,0.0001611515,0.001127564,0.0009230258,0.0002098723,0.0004986699,0.0008006907,0.00122231,0.0006415252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006149389,"about_ca_system_score_gemma":0.0009384804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004741004,"about_ca_topic_score_gemma":0.003629556,"domain_scores_codex":[0.9993105,0.0001525034,0.00007451684,0.0001801194,0.0001499618,0.0001323547],"domain_scores_gemma":[0.9988517,0.000543451,0.0001874751,0.00006910704,0.000235957,0.0001123292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001316739,0.0006527313,0.6678712,0.0001802813,0.0005164829,0.0005890151,0.0001773973,0.02385012,0.01493268,0.0004498598,0.005152483,0.2843111],"study_design_scores_gemma":[0.00006185148,0.0007655296,0.2075427,0.0001471997,0.0003972754,0.0007734323,0.0004956478,0.7707253,0.0144275,0.001605701,0.002989217,0.00006874821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9158907,0.002153601,0.07340462,0.001007463,0.0001740722,0.0001799133,0.00486791,0.0007929393,0.00152892],"genre_scores_gemma":[0.9496077,0.0003588432,0.04376866,0.0001904919,0.00006656786,0.0001533236,0.004836025,0.00003427577,0.0009841499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004741004,"threshold_uncertainty_score":0.01008189,"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."}}