Tracking the Metabolites of Health and Disease Using Artificial Intelligence
Open Access
Abstract
Using AI to analyze metabolite profiles provides critical insights into health, aging, and disease. Metabolomic signatures reveal how lifestyle and therapy impact organ function and cancer progression. This review highlights emerging toolkits for high-throughput data analysis, emphasizing their integration with other omics. Advanced AI approaches facilitate metabolic pathway mapping and accelerate biomarker discovery. By combining AI with multi-omics, researchers can optimize interventions and enhance precision medicine. This article serves as a resource demonstrating AI’s potential in diagnostics and drug discovery.Keywords
This publication has 128 references indexed in Scilit:
- Capillary Electrophoresis-Mass Spectrometry for Metabolomics: Possibilities and Perspectives2021
- Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care testsAnalytical and Bioanalytical Chemistry, 2021
- MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics dataMetabolomics, 2020
- Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data AnalysisCurrent Protocols in Bioinformatics, 2019
- Emerging Applications of Metabolomics in Clinical PharmacologyClinical Pharmacology & Therapeutics, 2019
- Towards quality assurance and quality control in untargeted metabolomics studiesMetabolomics, 2019
- Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studiesMetabolomics, 2018
- Critical review of reporting of the data analysis step in metabolomicsMetabolomics, 2017
- Navigating freely-available software tools for metabolomics analysisMetabolomics, 2017
- Proposed minimum reporting standards for chemical analysisMetabolomics, 2007