{ "abstract": "Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease that typically develops in early childhood. Differences in AD prevalence and allergy sensitisation patterns have been observed in African populations, including the AmaXhosa population in South Africa, suggesting alternative pathogenic and immune mechanisms underlying AD. Differences in AD prevalence have also been documented between urban and rural communities, making AmaXhosa children, who share a common ethnogenetic background but differ in environmental exposures, a unique population in which to investigate environmental and immune mechanisms underlying AD. To address this, we performed a machine learning (ML)-based multimodal observational study to identify features associated with AD in 217 AmaXhosa children. Methods and findings: To gain deeper insights into AD pathogenesis, we re-analysed a previously established multimodal dataset comprising environmental, cytokine, antibody, and transcriptomic data from healthy AmaXhosa children and children with AD, aged 12–36 months, living in rural or urban settings. We applied ML to analyse each data modality individually and subsequently integrate them to identify multimodal signatures associated with AD. Specifically, we used the GeneSelectR workflow to identify informative genes, SHAP values to explain the ML outputs, and DIABLO to integrate the datasets and identify protective and susceptibility clusters. Conclusions: Complementary ML approaches enabled explainable analysis of a complex multimodal dataset. Integrated analyses identified a multimodal protective cluster comprising environmental, cytokine, and transcriptomic features, together with two AD susceptibility clusters, one dominated by transcriptomic features and the other by correlated cytokine and antibody levels. These findings improve our understanding of the factors associated with AD. Furthermore, the feature-selection and integration workflow provides a framework for analysing complex multimodal datasets in future biomedical research. Why was this study done?: What did the researchers do and find?: What do these findings mean?: In this study, Damir Zhakparov and colleagues use machine learning to identify multimodal feature clusters associated with paediatric atopic dermatitis in AmaXhosa children in South Africa, highlighting a rural-environment-linked cluster associated with healthy skin and two susceptibility-related clusters.", "abstract_shown": true, "affiliations": [ "Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland", "Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland", "Division of Dermatology, University of Cape Town, Cape Town, South Africa", "APC Microbiome Ireland, University College Cork, Cork, Ireland", "University of Applied Sciences of the Grisons, Chur, Switzerland", "ETHZ, D-BSSE, Basel, Switzerland", "Christine Kühne-Center for Allergy Research and Education, Davos, Switzerland", "Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America", "Division of Paediatric Allergy, Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa", "Department of Medicine, University College Cork, Cork, Ireland", "School of Microbiology, University College Cork, Cork, Ireland" ], "article_type": "Research Article", "arxiv": null, "author_total": 14, "authors": [ { "affs": [ 0 ], "name": "Damir Zhakparov" }, { "affs": [ 1 ], "name": "Nonhlanhla Lunjani" }, { "affs": [ 2 ], "name": "Marco Schmid" }, { "affs": [ 3 ], "name": "Kathleen Moriarty" }, { "affs": [ 4 ], "name": "Damian Roquero" }, { "affs": [ 5 ], "name": "Anita Dreher" }, { "affs": [ 6 ], "name": "Jeannette I. Heldstab-Kast" }, { "affs": [ 7 ], "name": "Kari C. Nadeau" }, { "affs": [ 8 ], "name": "Cezmi Akdis" }, { "affs": [ 9 ], "name": "Michael Levin" }, { "affs": [ 10 ], "name": "Carol Hlela" }, { "name": "Milena Sokolowska" }, { "name": "Liam O’Mahony" }, { "name": "Katja Baerenfaller" } ], "bibtex": "https://research.draconitic.com/paper/1vkje0x365h4b.bib", "cited_by": 0, "doi": "10.1371/journal.pmed.1004917", "fulltext_url": "https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004917", "id": "1vkje0x365h4b", "issns": [ "1549-1676" ], "landing_url": "https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004917", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2708|Dermatology and Skin Diseases" ], [ "plos", "/Biology and life sciences/Computational biology/Genome analysis/Transcriptome analysis" ], [ "plos", "/Biology and life sciences/Developmental biology/Molecular development/Cytokines" ], [ "plos", "/Biology and life sciences/Genetics/Genomics/Genome analysis/Transcriptome analysis" ], [ "plos", "/Biology and life sciences/Immunology/Clinical immunology/Allergies" ], [ "plos", "/Biology and life sciences/Immunology/Immune system/Innate immune system/Cytokines" ], [ "plos", "/Biology and life sciences/Physiology/Immune physiology/Cytokines" ], [ "plos", "/Computer and information sciences/Artificial intelligence/Machine learning" ], [ "plos", "/Computer and information sciences/Network analysis" ], [ "plos", "/Ecology and environmental sciences/Terrestrial environments/Urban environments" ], [ "plos", "/Engineering and technology/Energy and power/Fuels" ], [ "plos", "/Medicine and health sciences/Clinical medicine/Clinical immunology/Allergies" ], [ "plos", "/Medicine and health sciences/Immunology/Clinical immunology/Allergies" ], [ "plos", "/Medicine and health sciences/Immunology/Immune system/Innate immune system/Cytokines" ], [ "plos", "/People and places/Population groupings/Age groups/Children" ], [ "plos", "/People and places/Population groupings/Families/Children" ], [ "plos", "/Physical sciences/Materials science/Materials/Fuels" ] ], "openalex": "W7211937723", "pdf_url": "https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1004917&type=printable", "pmcid": null, "pmid": null, "published": "2026-09-08", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/1vkje0x365h4b.ris", "sources": [ { "first_seen": "2026-10-30T12:17:56Z", "native_id": "10.1371/journal.pmed.1004917", "source": "plos", "url": "https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004917" } ], "subjects": [ { "field": "biology", "origin": "plos:immunology", "subfield": "immunology" } ], "title": "Multimodal protective and susceptibility clusters in paediatric atopic dermatitis: A machine learning-based, data-driven observational study", "title_text": "Multimodal protective and susceptibility clusters in paediatric atopic dermatitis: A machine learning-based, data-driven observational study", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:56Z", "url": "https://research.draconitic.com/paper/1vkje0x365h4b", "venue": "PLOS Medicine", "versions": [] }