Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Abstract

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

“…In the last few years, ML research and techniques have improved as large datasets generated by modern analytical lab instruments become available. Therefore, in recent reports we are starting to see ML-based research in identifying weight loss biomarkers [ 72 ], the discovery of food identity markers [ 73 ] farm animal metabolism [ 74 ] and many other applications in untargeted metabolomics [ 75 , 76 ]. In metabolic engineering, several areas are starting to take advantage of ML and systems biology integration including pathways identification and analysis, modeling of metabolisms and growth, and 3D protein modeling ( Figure 3 ).…”

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

“…Especially at the beginning of the data evaluation, it is often helpful to carry out an unsupervised method to get a first impression of the data and to recognize trends, groups and supposed outliers. A helpful listing of the various multivariate analysis methods along with their advantages and disadvantages was recently published by Liebal et al [ 163 ].…”

“…Additionally, in most cases, supervised methods have to be used to extract the most relevant compounds. Partial least square discriminant analysis (PLS-DA) is suitable for this purpose, which is currently the most frequently used supervised multivariate analysis method, as recently analyzed in a literature review [ 163 ]. PLS-DA is derived from PLS regression.…”

“…In recent years, RF and support vector machines (SVM) have also become more important in the supervised methods [ 163 ]. Both methods present the results in confusions matrices, in which the determined class affiliations of the samples are reproduced.…”

Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

“…Building upon the previous work on pathway-based modeling and prediction, Lilikoi v2.0 allows much better exploration of pathway-based analysis using various modern analytics methods for classification and survival analysis, including deep learning implementation. Such endeavor sets Lilikoi apart from other more conventional metabolomics analysis packages(36)(37)(38) .…”