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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Artificial Intelligence in Metabolomics: A Current Review.

Jinhua Chi1,2, Jingmin Shu1,3, Ming Li4,5

  • 1College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.

Trends in Analytical Chemistry : TRAC
|July 29, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and metabolomics are revolutionizing systems biology and human health. AI enhances the analysis of complex metabolomic data for improved biomarker discovery and predictive modeling.

Keywords:
Artificial IntelligenceDeep LearningDisease DiagnosisDrug DiscoveryMachine LearningMetabolomicsPrecision MedicineSystems Biology

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Area of Science:

  • Biomedical Informatics
  • Systems Biology
  • Metabolomics

Background:

  • Metabolomics generates vast, complex datasets of metabolites.
  • Artificial intelligence (AI) excels at analyzing large biological datasets.
  • Integrating AI with metabolomics offers new insights into human health.

Purpose of the Study:

  • To review AI methodologies and applications in metabolomics.
  • To highlight AI's role in systems biology and human health research.
  • To discuss current challenges and future directions for AI in metabolomics.

Main Methods:

  • Overview of AI concepts, machine learning, and deep learning algorithms.
  • Review of studies applying AI in metabolomic data analysis.
  • Discussion of AI's utility in analytical detection, data preprocessing, and multi-omics integration.

Main Results:

  • AI significantly enhances the analysis of complex metabolomic data.
  • AI facilitates biomarker discovery and predictive modeling in health studies.
  • AI aids in integrating multi-omics data for a holistic biological understanding.

Conclusions:

  • The synergy between AI and metabolomics promises significant advancements in human health.
  • AI offers powerful tools for unlocking the potential of metabolomic data.
  • Continued development is crucial for overcoming challenges and realizing AI's full impact in metabolomics.