Machine Learning Applications for Mass Spectrometry-Based Metabolomics.

Area of Science:

• Biochemistry
• Computational Biology
• Data Science

Background:

• Metabolomics, utilizing mass spectrometry (MS), offers insights into physiological states and disease progression.
• MS metabolome data analysis is complex due to nonlinear metabolite interactions and intricate data structures.
• Machine learning (ML) methods are increasingly vital for processing large, heterogeneous, and nonlinear biological data.

Purpose of the Study:

• To review recent advancements in applying ML to MS spectra processing in metabolomics.
• To demonstrate how ML generates novel biological insights from metabolomics data.
• To highlight the potential of supervised ML in quantitative predictions for metabolomics research.

Main Methods:

• Review of commonly used ML tools including random forest, support vector machines, artificial neural networks, and genetic algorithms.
• Application of ML in data processing steps such as peak picking, normalization, and missing data imputation.
• Integration of different omics data with ML for comprehensive analysis.

Main Results:

• Supervised ML methods aid in biomarker detection, classification, regression, pathway identification, and carbon flux determination.
• ML facilitates knowledge-driven analysis and enhances the interpretation of complex metabolomics datasets.
• Data quality is crucial for analysis outcomes, emphasizing the need for appropriate model selection.

Conclusions:

• ML applied to MS-based metabolomics simplifies data analysis, supporting clinical decisions and metabolic engineering.
• ML accelerates fundamental biological discoveries by extracting deeper insights from metabolomics data.
• The integration of ML is pivotal for advancing metabolomics research and its applications.