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September 23, 2025Deleted JournalOpen Access

Artificial Intelligence and Multi-Omics Integration in Obesity: A Review of Computational Models for Predicting Metabolic Comorbidities

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Review evaluates AI strategies for predicting metabolic disorders in obesity, highlighting multi-omics approaches and challenges.

Applying machine learning to omics data can improve prediction accuracy by up to 15% over existing methods.
Obesity is a major contributor to metabolic disorders, leading to millions of deaths annually from related diseases.
This review critiques various AI-driven multi-omics integration strategies and highlights their limitations.
Key considerations for clinical translation include data standardization, privacy, and regulatory compliance.

Saikia et al. (2025) studied this question.

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