Identification of metabolomics-based biomarker discovery in individuals with down syndrome utilizing kernel-tree model-enhanced explainable artificial intelligence methodology

Identification of metabolomics-based biomarker discovery in individuals with down syndrome utilizing kernel-tree model-enhanced explainable artificial intelligence methodology

Objective: This study aims to develop an explainable artificial intelligence (XAI) model integrated with machine learning (ML) to comprehensively investigate metabolic differences between individuals with Down syndrome (T21) and healthy controls (D21) and to identify novel/pathway-specific biomarker...

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Bibliographic Details
Main Authors: Cemil Colak, Fatma Hilal Yagin, Burak Yagin, Abedalrhman Alkhateeb, Mahmood Basil A. Al-Rawi, Moulay A. Akhloufi, Mohammadreza Aghaei
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Molecular Biosciences
Subjects:
down syndrome
metabolomics analysis
biomarker
machine learning
SHAP
KTBoost
Online Access:https://www.frontiersin.org/articles/10.3389/fmolb.2025.1567199/full
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https://www.frontiersin.org/articles/10.3389/fmolb.2025.1567199/full

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