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Integrating Artificial Intelligence into metabolomics for predicting diseases.

Dinesh Kumar1, Kashif R Siddique2, Rimjhim Trivedi1

  • 1Department of Advanced Spectroscopy and Imaging, Centre of Biomedical Research (CBMR), Lucknow, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.

Progress in Molecular Biology and Translational Science
|April 15, 2026
PubMed
Summary

Metabolomics detects early disease markers before symptoms appear. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), enhances the analysis of complex metabolic data for predictive and personalized medicine.

Keywords:
Artificial IntelligenceDeep LearningDisease predictionEarly disease diagnosisMachine LearningMetabolomics

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Area of Science:

  • Biomedical science
  • Computational biology
  • Metabolomics

Background:

  • Early disease diagnosis is crucial for effective treatment and better patient outcomes.
  • Metabolomics enables the detection of subtle metabolic shifts indicative of disease onset before clinical manifestation.
  • It offers insights into disease progression and underlying mechanisms, aiding biomarker discovery for various conditions.

Purpose of the Study:

  • To provide an overview of integrating Artificial Intelligence (AI) into metabolomics.
  • To address the analytical challenges posed by high-dimensional metabolomics data.
  • To explore AI-driven strategies for predictive and personalized medicine using metabolomics.

Main Methods:

  • Review of Artificial Intelligence (AI) applications in metabolomics workflows.
  • Discussion of machine learning (ML) and deep learning (DL) approaches for data analysis.
  • Examination of computational strategies for managing complex, high-dimensional metabolic data.

Main Results:

  • AI, ML, and DL methods are integrated into metabolomics to overcome data complexity and heterogeneity.
  • These computational strategies facilitate the extraction of meaningful insights from large-scale metabolic profiling.
  • Case studies and methodological advancements illustrate the utility of AI in disease prediction.

Conclusions:

  • AI significantly enhances the predictive power of metabolomics for early disease detection.
  • Advanced computational approaches are essential for realizing the potential of metabolomics in personalized medicine.
  • The integration of AI into metabolomics workflows represents a significant step forward in disease prediction and management.