A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children

Hendrickx D.M.; Savova M.V.; Zhu P.; An R.; Boeren S.; Klomp K.; Mutte S.K.; Wopereis H.; van der Molen R.G.; Harms A.C.; Belzer C.; Chatchatee P.; Nowak-Wegrzyn A.; Lange L.; Benjaponpitak S.; Chong K.W.; Sangsupawanich P.; van Ampting M.T.J.; Nijhuis M.M.O.; Harthoorn L.F.; Langford J.E.; Knol J.; Knipping K.; Garssen J.; Trendelenburg V.; Pesek R.; Davis C.M.; Muraro A.; Erlewyn-Lajeunesse M.; Fox A.T.; Michaelis L.J.; Beyer K.; Noimark L.; Stiefel G.; Schauer U.; Hamelmann E.; Peroni D.; Boner A.

A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children

Issued Date

2025-01-01

Resource Type

Article

eISSN

25154184

DOI

10.1039/d4mo00245h

Scopus ID

2-s2.0-105007456586

Pubmed ID

40407702

Journal Title

Molecular Omics

Rights Holder(s)

SCOPUS

Bibliographic Citation

Molecular Omics (2025)

Suggested Citation

Hendrickx D.M., Savova M.V., Zhu P., An R., Boeren S., Klomp K., Mutte S.K., Wopereis H., van der Molen R.G., Harms A.C., Belzer C., Chatchatee P., Nowak-Wegrzyn A., Lange L., Benjaponpitak S., Chong K.W., Sangsupawanich P., van Ampting M.T.J., Nijhuis M.M.O., Harthoorn L.F., Langford J.E., Knol J., Knipping K., Garssen J., Trendelenburg V., Pesek R., Davis C.M., Muraro A., Erlewyn-Lajeunesse M., Fox A.T., Michaelis L.J., Beyer K., Noimark L., Stiefel G., Schauer U., Hamelmann E., Peroni D., Boner A. A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children. Molecular Omics (2025). doi:10.1039/d4mo00245h Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/110679

Title

A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children

Corresponding Author(s)

Hendrickx D.M.

Other Contributor(s)

Mahidol University

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.

Keyword(s)

Biochemistry, Genetics and Molecular Biology

URI

https://repository.li.mahidol.ac.th/handle/123456789/110679

Collections

Scopus 2025

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