Innovative Approaches to Predicting Metabolic Diseases Using Bioinformatics and Machine Learning Technologies
Bioinformatics and Machine Learning Technologies
DOI:
https://doi.org/10.1071/ejmbs.v6i1.77Keywords:
bioinformatics, metabolomics, metabolic syndrome, machine learning, multi-omicsAbstract
Contemporary approaches to predicting metabolic diseases – such as metabolic syndrome, obesity, and type 2 diabetes – are increasingly grounded in bioinformatics methods, including genomic, metabolomic, and polygenic risk modeling, as well as machine learning and multi-omics data integration. This review highlights key trends in the field: the identification of genetic, transcriptomic, and metabolic biomarkers; the development of polygenic and multi-omics predictive models; the use of cohort and prospective studies; and methodological innovations aimed at improving accuracy and translational relevance. Particular emphasis is placed on machine learning approaches based on genetic and metabolomic data, exploring both their potential and limitations. Recent studies are discussed in which models incorporating genetic variation and dietary factors have achieved high predictive performance. Furthermore, the review examines how integrating “omics” data (genomics and metabolomics) with clinical features enhances prediction, captures inter-population variability, and enables responsiveness to lifestyle factors. Finally, future directions are outlined, including data standardization, increasing ethnic diversity in datasets, development of interpretable and clinically applicable models, and validation of intervention outcomes across independent cohorts.
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