Machine Learning-Based Validation of LDHC and SLC35G2 Methylation as Epigenetic Biomarkers for Food Allergy

Basit Öğe Kaydı
dc.authorid0009-0007-9299-7141
dc.authorid0000-0003-4303-9717
dc.authorid0000-0002-2186-6052
dc.authorid0000-0002-9292-5419
dc.authorid0000-0001-5957-1383
dc.authorid0000-0001-9483-4425
dc.contributor.authorKilicarslan, Sabire
dc.contributor.authorCicekliyurt, Meliha Merve Hiz
dc.contributor.authorKilicarslan, Serhat
dc.contributor.authorHassan, Dina S. M.
dc.contributor.authorSamee, Nagwan Abdel
dc.contributor.authorKurtoglu, Ahmet
dc.date.accessioned2026-02-03T12:00:01Z
dc.date.available2026-02-03T12:00:01Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractBackground: Food allergies represent a growing global health concern, yet the current diagnostic methods often fail to distinguish between true allergies and food sensitivities, leading to misdiagnoses and inadequate treatment. Epigenetic alterations, such as DNA methylation (DNAm), may offer novel biomarkers for precise diagnosis. Methods: This study employed a computational machine learning framework integrated with DNAm data to identify potential biomarkers and enhance diagnostic accuracy. Differential methylation analysis was performed using the limma package to identify informative CpG features, which were then analyzed with advanced algorithms, including SVM (polynomial and RBF kernels), k-NN, Random Forest, and artificial neural networks (ANN). Deep learning via a stacked autoencoder (SAE) further enriched the analysis by uncovering epigenetic patterns and reducing feature dimensionality. To ensure robustness, the identified biomarkers were independently validated using the external dataset GSE114135. Results: The hybrid machine learning models revealed LDHC and SLC35G2 methylation as promising biomarkers for food allergy prediction. Notably, the methylation pattern of the LDHC gene showed significant potential in distinguishing individuals with food allergies from those with food sensitivity. Additionally, the integration of machine learning and deep learning provided a robust platform for analyzing complex epigenetic data. Importantly, validation on GSE114135 confirmed the reproducibility and reliability of these findings across independent cohorts. Conclusions: This study demonstrates the potential of combining machine learning with DNAm data to advance precision medicine in food allergy diagnosis. The results highlight LDHC and SLC35G2 as robust epigenetic biomarkers, validated across two independent datasets (GSE114134 and GSE114135). These findings underscore the importance of developing clinical tests that incorporate these biomarkers to reduce misdiagnosis and lay the groundwork for exploring epigenetic regulation in allergic diseases.
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia [PNURSP2025R751]
dc.description.sponsorshipThis research was funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project, grant number PNURSP2025R751, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The APC was funded by the same institution.
dc.identifier.doi10.3390/biomedicines13102489
dc.identifier.issn2227-9059
dc.identifier.issue10
dc.identifier.pmid41153772
dc.identifier.scopus2-s2.0-105020312949
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/biomedicines13102489
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34482
dc.identifier.volume13
dc.identifier.wosWOS:001601836200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofBiomedicines
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260130
dc.subjectfood allergy
dc.subjectDNA methylation
dc.subjectLDHC
dc.subjectmachine learning
dc.subjectepigenetics
dc.subjectbiomarker
dc.titleMachine Learning-Based Validation of LDHC and SLC35G2 Methylation as Epigenetic Biomarkers for Food Allergy
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu