Integrating Metabolomics and Machine Learning for Advanced Chemical Detection

Area of Science:

• Analytical Chemistry
• Bioinformatics
• Computational Biology

Background:

• Metabolomics provides comprehensive chemical profiles of biological and environmental samples.
• Increasing data complexity necessitates advanced analytical techniques for interpretation.

Purpose of the Study:

• To critically review the integration of machine learning (ML) with metabolomics for advanced chemical detection.
• To emphasize analytical workflows, data preprocessing, ML models, and validation strategies.

Main Methods:

• Narrative review of existing literature on metabolomics and ML integration.
• Discussion of supervised and unsupervised learning models.
• Analysis of applications in food science, environmental monitoring, clinical diagnostics, and exposomics.

Main Results:

• ML techniques significantly enhance chemical detection, classification, and interpretation in metabolomics.
• Advanced chemical detection involves data-driven identification, classification, and quantification with improved metrics.
• Current applications demonstrate the utility of ML-metabolomics across diverse scientific fields.

Conclusions:

• The synergy between metabolomics and ML offers powerful capabilities for analyzing complex chemical data.
• Key challenges include data quality, interpretability, and reproducibility, requiring further research.
• Future directions involve explainable AI, multimodal data integration, and standardized analytical pipelines.