ArticleMolecular omics2025
A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children.
Article in Molecular omics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Deep learning-driven automatic counting of petal number in cut chrysanthemum inflorescence.Plant phenomics (Washington, D.C.) · 2026Article
- Research Progress in Multi-Omics Analysis of Dairy Products: Nutritional Quality, Safety Evaluation, and Health Functions.Foods (Basel, Switzerland) · 2026Review
- Multi-omics integration in allergy, viral infections and asthma: a comprehensive analysis of bioinformatics tools.Frontiers in bioinformatics · 2026Article
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Authors and funding
12 authors.
Funding
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Abstract
Cow's milk protein allergy (CMA) is one of the most common food allergies in children worldwide. However, it is still not well understood why certain children outgrow their CMA and others do not. While there is increasing evidence for a link of CMA with the gut microbiome, it is still unclear how the gut microbiome and metabolome interact with the immune system. Integrating data from different omics platforms and clinical data can help to unravel these interactions. In this study, we integrate clinical, microbial, (meta)proteomics, immune and metabolomics data into machine learning (ML) classification, using multi-view learning by late integration. The aim is to group infants into those that outgrew their CMA and those that did not. The results show that integration of microbiome data with clinical, immune, (meta)proteomics and metabolomics data could considerably improve classification of infants on outgrowth of CMA, compared to only considering one type of data. Moreover, pathways previously linked to development of CMA could also be related to outgrowth of this allergy.
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