AI and ML in Untargeted Metabolomics and Exposomics
Metabolomics and exposomics use AI and ML to analyze biological samples, providing insights into human health and disease. AI enhances untargeted metabolomics workflows, improving data quality and chemical identification, leading to major disease screening and diagnosis findings.
Untargeted Metabolomics Workflow
The workflow involves separating complex mixtures, followed by high-resolution mass spectrometry (HRMS) detection and measurement. AI and ML tools play crucial roles in data processing, feature selection, and chemical identification, enhancing the analysis of metabolomics data and its biological interpretation.
Data Processing in Untargeted Metabolomics
Metabolomics raw data are complex, and pre-processing is crucial for downstream analysis. AI and ML tools promise to improve data processing accuracy and reliability.
AI and ML in Biomarker Discovery
AI and ML methods uncover relationships between phenotypes, exposures, and diseases, successfully identifying significant biomarkers and metabolite signatures.
Metabolite Identification in Biomarker Discovery
Advances in cognitive metabolomics using ML and NLP are improving identification accuracy, crucial for better understanding of both endogenous and exogenous chemicals.
Advances in Untargeted Chemical Analysis
AI and ML tools have significantly reduced costs and enabled large-scale studies, aiding in data extraction, mining, and annotation. Efforts focus on developing experimental databases and AI/ML models to enhance metabolite identification.
Leveraging AI and Machine Learning ML for Untargeted Metabolomics and Exposomics: Advances, Challenges, and Future Directions
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