Table of Contents
Overview
Definition of Metabolomics
Metabolomics is defined as the large-scale study of small molecules, known as metabolites, within biological systems such as cells, biofluids, tissues, or organisms. Collectively, these metabolites and their interactions form what is referred to as the metabolome.[2.1] This field encompasses the comprehensive and quantitative analysis of all metabolites, which is essential for understanding the extensive network of metabolic reactions that occur within biological systems.[1.1] Metabolomics serves as a significant component of systems biology, extending beyond simple metabolic profiling and biomarker discovery to include the analysis of how metabolites respond to various external stimuli.[5.1] The study of metabolomics is particularly valuable as it provides insights into the biochemical processes that underpin cellular functions and phenotypes, thereby reflecting the downstream effects of the central dogma of molecular biology.[7.1] By analyzing metabolites, researchers can identify biomarkers for early detection of diseases such as cancer, diabetes, and cardiovascular disorders, as well as explore applications in nutrition science and environmental studies.[5.1] Moreover, metabolomics data analysis is crucial for interpreting the vast datasets generated during experiments, allowing for the extraction of meaningful insights from complex metabolite profiles.[4.1] The analytical techniques commonly employed in metabolomics include nuclear magnetic resonance (NMR) and mass spectrometry (MS), both of which have unique strengths and can be used in tandem to enhance the accuracy of metabolite identification.[17.1] The integration of these techniques facilitates a more comprehensive understanding of the metabolome, which is essential for advancing precision medicine and other applications in biological research.[6.1]Importance in Systems Biology
The integration of metabolomics with other omics technologies, such as genomics and proteomics, plays a crucial role in advancing our understanding of biological systems and disease mechanisms. By combining data from these various biological domains, researchers can gain a more comprehensive view of cellular functions and the intricate interactions that govern health and disease. This multi-omics approach allows for the simultaneous study of thousands of proteins, genes, RNAs, and metabolites, thereby enhancing the depth of biological insights derived from analyses.[22.1] One of the significant advantages of integrating metabolomics with genomics and proteomics is its ability to elucidate metabolic pathways and identify the causes of metabolic changes. For instance, metabolomics can uncover metabolic bottlenecks in genetically modified organisms and host-microbe interactions, providing insights that are not readily apparent through genomic or proteomic data alone.[9.1] Furthermore, the integration of these omics layers contributes to systems biology approaches, enabling the construction of comprehensive models that capture the interactions between genes, transcripts, proteins, and metabolites.[23.1] This holistic view is essential for understanding the complex molecular underpinnings of health and disease, as well as the chain of cause and effect necessary for guiding novel therapies.[8.1] Recent advancements in artificial intelligence (AI) have further enhanced the potential of metabolomics in precision oncology. AI-driven metabolomics can accelerate the translation of research into clinical applications, offering deeper insights into disease mechanisms and facilitating the development of personalized treatments.[10.1] For example, AI techniques have been employed to analyze multi-omics data in diabetes care, predicting insulin responses and allowing for personalized dosing strategies.[18.1] Such applications underscore the transformative impact of integrating metabolomics with other omics technologies in clinical practice. Moreover, various methods and tools have been developed to facilitate the integration of metabolomic data with genomic and proteomic information. These include biochemical pathway-, ontology-, network-, and empirical-correlation-based methods, which collectively enhance the analysis and interpretation of complex biological data.[19.1] Tools like Metscape and Meta-MapR exemplify the innovative approaches being utilized to visualize and analyze gene-to-metabolite networks, further enriching our understanding of metabolic processes.[20.1]In this section:
History
Ancient Practices in Metabolomics
Metabolomics has evolved significantly from ancient practices that relied on qualitative observations of bodily fluids to a modern framework that emphasizes quantitative analysis of metabolites. In ancient times, the examination of bodily fluids was primarily qualitative, focusing on observable characteristics rather than precise measurements. This approach limited the understanding of health and disease, as it did not provide a comprehensive view of the biochemical processes occurring within the body. The transition to modern metabolomics marks a pivotal shift, characterized by the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids, and tissues, collectively known as the metabolome. This advancement allows for the identification of differences between metabolomes using biostatistics, including multivariate data analysis and pattern recognition, which can reveal metabolites relevant to specific phenotypic characteristics.[50.1] Modern techniques, particularly mass spectrometry (MS), have played a crucial role in this transition. The development of advanced mass spectrometers, such as QqQ and Orbitrap, enables the simultaneous quantitation of tens to hundreds of metabolites with high ionization efficiency and faster spectral scan rates.[51.1] These technological improvements facilitate a more detailed understanding of metabolic profiles, which is essential for the discovery and enhancement of clinical strategies aimed at treating human diseases.[52.1] As metabolomics continues to advance, it is expected to become a routine tool for diagnosing and monitoring health, disease, aging, and drug development, further bridging the gap between ancient qualitative practices and contemporary quantitative methodologies.[52.1]Development of the Term and Field
The term "metabolome" was introduced in 1998, marking a significant milestone in the evolution of the field, which was previously referred to as metabolic phenotyping or metabonomics. This term was coined by analogy to other "-ome" terms such as genome and proteome, and it was during this period that the term "metabolomics" was first utilized in published literature by S.G. Oliver and his colleagues in the journal Trends in Biotechnology.[47.1] The development of metabolomics as a distinct scientific discipline was facilitated by advancements in analytical chemistry techniques, particularly nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). These technologies allowed for the comprehensive profiling of metabolites, enabling researchers to detect complex biological changes and variations in metabolite profiles.[70.1] The integration of these methods has been crucial in establishing metabolomics as an interdisciplinary field that combines elements of biochemistry, analytical chemistry, and bioinformatics.[70.1] In the years following the introduction of the term, the field saw a rapid expansion, with the first research papers employing the terms metabolomics, metabonomics, and metabolic profiling emerging shortly thereafter.[47.1] The application of metabolomics has since grown, particularly in drug research and development, where it has proven invaluable in understanding disease mechanisms, identifying drug targets, and elucidating the modes of action of drugs.[59.1]In this section:
Recent Advancements
Technological Innovations
Recent advancements in metabolomics have been significantly driven by technological innovations, particularly in analytical techniques. Mass spectrometry (MS) has emerged as a key technology in the field, enabling the detection and identification of small molecules produced by various biological systems, including the human microbiome. This technique has proven invaluable for understanding the functional roles of microbial metabolites and their implications in health and disease.[104.1] Alongside MS, nuclear magnetic resonance (NMR) spectroscopy has also gained prominence, providing complementary insights into the metabolome.[103.1] The evolution of metabolomics has been characterized by the integration of advanced analytical tools that enhance the qualitative and quantitative analysis of metabolites. For instance, the combination of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) has become increasingly prevalent, allowing for comprehensive profiling of complex biological samples.[102.1] These methodologies facilitate the exploration of metabolic networks and interactions within microbial communities, thereby advancing our understanding of ecological and health-related processes.[106.1] Moreover, the application of metabolomics extends beyond microbial studies; it plays a crucial role in various fields such as drug research and development, where it aids in identifying drug targets and elucidating mechanisms of action.[109.1] The integration of bioinformatics and computational tools further enhances the capabilities of metabolomics by streamlining data acquisition and processing, which is essential for metabolite identification and analysis.[108.1] In plant science, metabolomics techniques have been employed to investigate plant responses to environmental stressors, showcasing the versatility of these technologies across different biological domains.[111.1] Overall, the continuous development of sophisticated analytical techniques in metabolomics not only enriches our understanding of metabolic processes but also opens new avenues for research and application in health, agriculture, and environmental sciences.[110.1]Applications in Health and Disease
Recent advancements in metabolomics have significantly influenced the development of biomarkers for disease diagnosis, particularly in areas such as cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. These biomarkers facilitate early diagnosis, treatment response monitoring, and disease surveillance, thereby advancing personalized and precision oncology.[94.1] The ability of metabolomics to detect changes in metabolic profiles allows for the identification of novel biomarkers, which can be crucial for diagnosing complex diseases like cancer and autism spectrum disorder.[96.1] Metabolomics provides a unique metabolic readout that links genotype, environment, and phenotype, making it an attractive tool for clinical diagnosis, prognosis, and disease classification.[96.1] The identification of differential metabolites enhances the specificity and accuracy of biomarkers, which are essential for patient diagnosis, monitoring, risk prediction, and prognosis.[96.1] Furthermore, the integration of metabolomic data with genomic and proteomic information has been shown to improve our understanding of biological systems and disease mechanisms, allowing for a more comprehensive approach to health and disease.[112.1] Bioinformatics tools play a critical role in the interpretation of metabolomic data, enabling researchers to analyze complex datasets and derive meaningful insights. For instance, software like Metscape and Meta-MapR supports the visualization and integration of gene-to-metabolite networks, enhancing the understanding of metabolic pathways and their implications in health.[112.1] This integration of multi-omics data, including metabolomics, transcriptomics, and proteomics, is recommended for a holistic view of biological processes and disease mechanisms.[112.1]In this section:
Methodologies
Analytical Techniques
Metabolomics employs a variety of analytical techniques to separate, identify, and quantify metabolites, which are crucial for understanding metabolic pathways and biological processes. The primary methodologies include Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS), Nuclear Magnetic Resonance (NMR) spectroscopy, and Direct Infusion Mass Spectrometry (DI-MS) among others. Each technique has its own advantages and limitations, necessitating the use of multiple methods to achieve a comprehensive view of the metabolome within a biological sample.[134.1] GC-MS and LC-MS are particularly prominent in current metabolomic investigations. These techniques enable precise biomarker detection and metabolic pathway analysis, which are essential for advancements in personalized medicine and drug metabolism.[140.1] The integration of high-resolution mass spectrometry with these methodologies enhances the ability to analyze complex biological systems, facilitating breakthroughs in medical diagnostics and drug development.[140.1] Furthermore, the application of chemometric tools alongside these analytical platforms provides a holistic view of metabolism, allowing researchers to explore how lifestyle and dietary factors influence diseases through low-molecular-weight metabolite measurements.[138.1] In addition to traditional techniques, emerging methodologies such as single-cell metabolomics are gaining traction. This approach allows for the exploration of cellular metabolism at an unprecedented resolution, revealing intricate metabolic variations that contribute to biological diversity.[139.1] By employing advanced sampling and analytical techniques, single-cell metabolomics can elucidate metabolic pathways that support processes like stem cell differentiation, thereby enhancing our understanding of cellular heterogeneity.[139.1]Data Analysis Approaches
Machine learning (ML) has emerged as a pivotal tool in the analysis of metabolomics data, significantly enhancing the extraction of meaningful insights from complex datasets. By integrating domain-specific knowledge from metabolomics with explainable ML methods, researchers can improve both the predictive performance and interpretability of models, particularly in the context of atherosclerosis research.[146.1] Various ML methodologies are employed in metabolomics, including unsupervised and supervised analyses. Unsupervised methods aim to group samples or features without user bias, while supervised methods focus on developing classification models based on user-labeled training data.[148.1] In addition to traditional ML techniques, deep learning approaches have been applied to mass spectrometry data analysis, enabling the differentiation of healthy individuals from patients with various lung conditions. For instance, the DeepMSProfiler model processes untargeted liquid chromatography-mass spectrometry (LC-MS) raw data to identify disease-specific metabolic profiles, demonstrating the potential of deep learning in revealing metabolites and proteins associated with multiple cancer types.[149.1] Moreover, the integration of machine learning with genomics and metabolomics data has facilitated the optimization of metabolic network models. This integration allows for the calculation of parameters essential for stoichiometric analyses, further enhancing the understanding of metabolic pathways.[147.1] As the field progresses, various bioinformatics tools have been developed to support the integration of metabolomics data with other omics data, such as genomics and proteomics. Tools like Multi-Omics Factor Analysis (MOFA) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO) exemplify the advanced methodologies available for multi-omics integration, ensuring high interpretability of the common sources of variation among different omics datasets.[168.1] Additionally, user-friendly applications like EasyOmics provide biologists with the capability to perform population-scale omics data association, integration, and visualization without requiring extensive coding expertise.[169.1]In this section:
BiomarkerMetabolic Pathway AnalysisPersonalized MedicineDrug MetabolismComplex Biological Systems
Applications
Biomedical Research
Metabolomics plays a crucial role in biomedical research by providing insights into the dynamic molecular changes occurring within biological systems. It is defined as the systematic study of the metabolome, which encompasses the entirety of metabolites involved in metabolic reactions. This approach allows researchers to understand how specific metabolites influence biochemical pathways and contribute to various physiological and pathological states.[173.1] One of the primary applications of metabolomics in biomedical research is the identification and validation of disease biomarkers. By analyzing metabolic profiles, researchers can detect early signs of diseases such as cancer, diabetes, and cardiovascular disorders, thereby facilitating early diagnosis and intervention.[172.1] For instance, targeted metabolomic profiling has successfully identified novel metabolic biosignatures that predict cardiovascular disease events independently of traditional protein biomarkers.[193.1] Moreover, metabolomics is increasingly integrated with other omics technologies, such as genomics and proteomics, to enhance the understanding of complex diseases. This integration allows for the development of predictive models that can identify potential biomarkers for various diseases, improving patient stratification and personalized treatment approaches.[180.1] In clinical settings, metabolomics can also aid in tracking patient responses to treatments and identifying sub-phenotypes of diseases, which is particularly valuable in conditions like sepsis.[181.1] The application of metabolomics extends to pharmacometabolomics, which combines genetic and environmental factors to optimize drug dosage and improve treatment efficacy and safety.[180.1] This emerging field underscores the potential of metabolomics to guide patient selection in clinical trials, ultimately leading to more personalized medicine.[180.1]In this section:
Challenges And Limitations
Technical Challenges
Mass spectrometry (MS)-based metabolomics faces several technical challenges that hinder its effectiveness in providing comprehensive biological insights. One significant issue is the complexity of biological mixtures, which complicates the identification of unknown metabolites and the validation of biomarkers. The high diversity of dietary compounds and their metabolites further exacerbates this challenge, as many metabolites remain uncharacterized or are difficult to differentiate from environmental contaminants.[217.1] Another technical limitation arises from the reliance on retention time (RT) predictions for untargeted metabolomics annotation. This approach often necessitates a pre-characterized chromatographic system, and the effectiveness of RT projections is typically confined to similar liquid chromatography (LC) methods. The presence of closely eluting compounds can lead to crowded chromatograms, making it difficult to accurately identify and quantify metabolites.[226.1] Moreover, the lack of comprehensive metabolite libraries and the variability in fragmentation patterns across different instrument parameters contribute to the challenges in metabolite identification. Many features in metabolomic datasets remain unannotated, often referred to as the "dark matter" of the metabolome, which complicates data interpretation and analysis.[216.1] To address these challenges, innovative strategies such as the implementation of a four-dimensional identification approach have been proposed. This method has shown promise in improving the accuracy and coverage of metabolite identification, particularly for isomeric metabolites.[227.1] Additionally, employing various separation techniques, including liquid chromatography (LC), gas chromatography (GC), and capillary electrophoresis (CE), can enhance the identification process in untargeted metabolomics studies.[228.1]Translational Opportunities
Translational opportunities in metabolomics are significantly enhanced through the integration of advanced computational techniques, particularly machine learning and artificial intelligence (AI). These technologies facilitate the analysis of complex metabolomic data, enabling improved classification, regression, and clustering of metabolites, which are essential for clinical applications and precision medicine.[240.1] Recent advancements in machine learning have demonstrated their potential to enhance data analysis and disease classification, thereby improving metabolite identification and diagnostic methods.[241.1] Moreover, machine learning applications in mass spectrometry-based metabolomics have shown promise in addressing the limitations of current metabolite libraries and enhancing the identification of previously uncharacterized metabolites, often referred to as "dark matter".[233.1] The use of deep learning, particularly in data pre-processing, has also been recognized as beneficial, although its application in metabolomics remains relatively limited compared to other omics fields.[239.1] The integration of AI into metabolomics not only optimizes data acquisition and analysis but also supports the discovery of biomarkers and the monitoring of treatment responses in precision oncology.[243.1] Furthermore, the application of algorithms, including multivariate and machine learning methods, is crucial for managing the large number of variables and samples inherent in metabolomic studies.[242.1] As metabolomics continues to evolve, the development of novel metabolite annotation strategies, such as molecular networking and machine learning-based in-silico tools, is expected to enhance the reliability and scale of metabolite characterization.[252.1] These innovations are essential for uncovering the complexities of metabolic pathways and improving the overall understanding of biological processes, thereby paving the way for advancements in clinical diagnostics and therapeutic strategies.[251.1]In this section:
Liquid ChromatographyDark MatterSeparation TechniquesGas ChromatographyCapillary Electrophoresis
Future Directions
Emerging Trends
Recent advancements in metabolomics have significantly enhanced its application in drug research and development, particularly in early drug development phases. Metabolomics has evolved into a widely accepted approach, assisting in defining physiological responses and target engagement markers, as well as elucidating the mode of action of drug candidates under investigation.[255.1] This evolution has been facilitated by the integration of advanced analytical techniques, which have allowed metabolomics to play a crucial role in understanding disease mechanisms, identifying drug targets, and predicting drug responses, thereby enabling personalized treatment strategies.[257.1] Moreover, the integration of artificial intelligence (AI) and machine learning (ML) into metabolomics is emerging as a transformative trend. These technologies enhance data analysis, improve metabolite identification, and facilitate the integration of multi-omics datasets, which is essential for understanding complex biological systems.[266.1] AI-driven approaches have shown promise in various applications, including biomarker discovery, predictive modeling, and the optimization of data acquisition and analysis processes.[269.1] For instance, AI techniques have been successfully employed in analyzing multi-omics data to predict insulin responses in diabetes care and to identify therapeutic targets in Alzheimer's disease.[270.1] Furthermore, the future of metabolomics is likely to involve a more holistic approach, integrating metabolomics with other profiling modalities such as spatial omics and proteomics. This integration aims to provide a comprehensive understanding of biological systems and improve clinical outcomes.[258.1] As metabolomics continues to advance, its role in precision health and disease diagnosis is expected to expand, particularly in the context of personalized medicine and oncology.[256.1] The ongoing development of computational techniques and algorithms will further enhance the capabilities of metabolomics, paving the way for innovative solutions in drug discovery and disease management.[268.1]Integration with Other Omics Approaches
The integration of metabolomics with other omics technologies, such as genomics and proteomics, is crucial for advancing drug development and precision medicine. This multi-omics approach enables researchers to delve into complex biological networks, enhancing the understanding of disease mechanisms and drug responses.[263.1] Metabolomics, which analyzes small molecules and biochemical intermediates, offers insights into cellular processes and metabolic pathways essential for identifying new drug targets and elucidating mechanisms of action.[273.1] Technological advancements, including artificial intelligence (AI) and single-cell metabolomics, are being leveraged to improve the integration of metabolomic data with genomic and proteomic information.[274.1] AI-driven analyses have shown potential in predicting patient responses and tailoring personalized treatment strategies, particularly in oncology and diabetes care.[275.1] The systematic study of metabolites through metabolomics can reveal disease-related alterations, providing insights into disease etiology, therapeutic responses, and biological process changes.[261.1] This integration supports the identification of clinically applicable biomarkers and the development of safer, more effective individualized drug treatments.[271.1] As the field evolves, combining metabolomics with other omics data is expected to create unprecedented opportunities for precision medicine, utilizing a patient's unique biological information to optimize disease prevention, diagnosis, and treatment.[271.1] The ongoing development of high-throughput instruments and advanced data integration strategies will further enhance metabolomics' capabilities in understanding complex biological systems.[286.1]In this section:
References
[1] Metabolomics - an overview | ScienceDirect Topics — Metabolomics is the systematic study of a metabolome, the entirety of metabolites, or a set of metabolites, forming an extensive network of metabolic reactions in which one metabolite from a specific pathway will affect one or more biochemical reactions, or a comprehensive and quantitative analysis of all metabolites (Fiehn, 2001; Oliver et al., 1998). Therefore, metabolomics, as a major part of systems biology, is a far more profound concept and practice than simple metabolic profiling and biomarker search, although comprehensive, qualitative, and quantitative analyses of metabolites are the essential and common procedures in metabolomics studies. Metabolomics is an extension of genomics and proteomics, which studies the changes in all metabolites produced by external stimuli in biological systems (cells, tissues, or organisms).
[2] What is metabolomics? | Metabolomics - EMBL-EBI — What is metabolomics? | Metabolomics Metabolomics Metabolomics ------------An introduction Course overview What is metabolomics?Open Tree The metabolome and metabolic reactions Applications of metabolomics Designing a metabolomics studyOpen Tree Overview of complete analysis workflow Metabolomics resources at EMBL-EBI Metabolomics quiz What is metabolomics? Metabolomics is the large-scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues or organisms. Collectively, these small molecules and their interactions within a biological system are known as the metabolome. Figure 1 Overview of the four major “omics” fields, from genomics to metabolomics You have completed this tutorial. Continue on to the final pages of this online tutorial for recommendations on what to learn next and to tell us what you thought of this tutorial.
[4] What is Metabolomics - Creative Proteomics — Metabolomics data analysis is a crucial step in the field of metabolomics, which involves processing and interpreting the vast amount of data generated by metabolomics experiments. This process is essential for extracting meaningful insights from complex metabolite profiles. Metabolomics data analysis typically consists of several key steps:
[5] What is Metabolomics? Definition, Techniques, and Uses — What is Metabolomics and its applications? Untargeted metabolomics service Metabolomics Applications What is metabolomics? Metabolomics What is Metabolomics and its applications? Metabolomics, the study of small molecules in biological systems, is revolutionizing our At its core, metabolomics is the study of metabolites – the small molecules that are What Systems Can Metabolomics Study? Researchers use metabolomics to identify biomarkers for early detection of diseases like cancer, diabetes, and cardiovascular disorders. Metabolomics applications in nutrition science and environmental studies. Which Methods Are Used in Metabolomics? Targeted Metabolomics Untargeted Metabolomics By employing semi-targeted metabolomics, Arome Science offers clients a powerful and flexible tool that combines the best aspects of both untargeted and targeted approaches. What is metabolomics? Metabolomics in Environmental Studies
[6] Metabolomics: an emerging but powerful tool for precision medicine — Because metabolomics affords profiling of much larger numbers of metabolites than are presently covered in standard clinical laboratory techniques, and hence comprehensive coverage of biological processes and metabolic pathways, it holds promise to serve as an essential objective lens in the molecular microscope for precision medicine. In addition, the degree of certainty in metabolite identification can vary among methods, ranging from metabolite identities rigorously confirmed using authentic reference standards to putative identifications made using reference databases to signals that remain as “unknowns.” The need for standardization in metabolomics has been appreciated by its practitioners and has given rise to a number of initiatives toward realizing this aim, such as the Metabolomics Standards Initiative to develop guidelines for data reporting (Sansone et al.
[7] Metabolomics - an overview | ScienceDirect Topics — Metabolomics is the youngest and possibly the most promising "omics" field that helps to understand the downstream effects of the central dogma and directly reflecting the cellular phenotypes (Fig. 1).Metabolomics and other "omic" technologies (genomics, epigenomics, transcriptomics, proteomics, and microbiomics) provide a revolutionary means to investigate and assess diagnosis and
[8] Multi-omics integration in biomedical research - A metabolomics-centric ... — This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Therefore, combining omics data from multiple biological domains (e.g., levels of transcripts, proteins, or metabolites) in multi-omics studies is a promising approach towards a more detailed molecular understanding of health and disease, as well as the chain of cause and effect, which is an essential requirement for guiding novel therapies . In this review, we will provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with more than two omics and highlighting their application in recent multi-omics studies. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data
[9] Integration of metabolomics and other omics: from microbes to ... — Therefore, metabolomics is widely used as one of the most powerful tools to discover unknown metabolic pathways and identify the causes of metabolic changes, such as metabolic bottlenecks in genetically modified microorganisms and host-microbe interactions (Jeon et al. Metabolomics can provide insight into metabolic bottlenecks by tracking the levels of these cofactor or byproduct metabolites (Kim et al. Additionally, as genomics, transcriptomics, and fluxomics provide information regarding all genes or transcripts or use models that are built based on them, all metabolic changes can be investigated (Wörheide et al. Thus, whole-genome sequencing and metabolomics are frequently applied to a single organism to understand the metabolic changes caused by mutations in each gene and to identify the causes of physiological differences (Hou et al.
[10] Synergizing metabolomics and artificial intelligence for advancing ... — Synergizing metabolomics and artificial intelligence for advancing precision oncology - ScienceDirect By accelerating the translation of research into clinical applications, AI-driven metabolomics holds the potential to advance precision oncology despite current implementation challenges. In this opinion, we explore recent advances in the application of metabolomics within precision oncology, emphasizing the unique advantages that AI-driven metabolomics offers. Finally, we discuss the opportunities and challenges involved in translating AI-driven metabolomics into clinical practice for precision oncology. AI-driven metabolomics for precision oncology Integrating AI-driven metabolomics into oncology holds promise for improving patient outcomes and advancing precision oncology. AI-driven metabolomics has rapidly become an invaluable tool in precision oncology, which can offer deeper insights into disease mechanisms and accelerate the development of personalized treatments.
[17] Beyond the paradigm: Combining mass spectrometry and nuclear magnetic ... — Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are the analytical tools that are routinely, but separately, used to obtain metabolomics data sets due to their versatility, accessibility, and unique strengths.
[18] AI in Multi-Omics: Integrating Metabolomics, Proteomics, Genomics — AI In Multi-Omics: Integrating Metabolomics, Proteomics, Genomics AI in Multi-Omics: Integrating Metabolomics, Proteomics, Genomics How AI Revolutionizes Multi-Omics AI Techniques for Multi-Omics Integration For example, in diabetes care, AI analyzes multi-omics data to predict insulin response, allowing personalized dosing strategies. A notable example is AI-driven research into Alzheimer’s disease, where AI pinpointed specific proteins for therapeutic targeting based on multi-omics analysis. How reliable is AI-driven multi-omics research? Robust AI tools like DeepOmics have demonstrated high accuracy, such as predicting patient survival rates in cancer trials by integrating multi-omics data. AI-integrated multi-omics is revolutionizing environmental studies. Can AI improve healthcare access through multi-omics? Yes, AI enables cost-effective diagnostics by analyzing multi-omics data for early disease detection. Nature Biotechnology: Publishes cutting-edge research on multi-omics integration and AI applications in biology.
[19] Genomic, Proteomic, and Metabolomic Data Integration Strategies — Genomic, Proteomic, and Metabolomic Data Integration Strategies - PubMed Search in PubMed Genomic, Proteomic, and Metabolomic Data Integration Strategies Genomic, Proteomic, and Metabolomic Data Integration Strategies Search in PubMed This review focuses on select methods and tools for the integration of metabolomic with genomic and proteomic data using a variety of approaches including biochemical pathway-, ontology-, network-, and empirical-correlation-based methods. Keywords: bioinformatics; data analysis; data integration; genomics; metabolomics; networks; omics; proteomics. Example of a modern metabolomic data analysis workflow integrating three discreet mass spectral analysis platforms. Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine. Search in PubMed
[20] Genomic, Proteomic, and Metabolomic Data Integration Strategies — Metscape,17 a plug-in for the widely used network analysis software Cytoscape,18 supports calculation, analysis, and visualization of gene-to-metabolite networks in the context of metabolism.17 Another software, Meta-MapR,9 leverages the KEGG19 and PubChem20 databases to provide methods for integration and visualization of complex metabolomic results even in cases where biochemical domain knowledge or molecular annotations are unknown.9 For example, MetaMapR has been used to integrate both biochemical reaction information with molecular structural and mass spectral similarity to identify pathway-independent relationships, including, between molecules with unknown structure or biological function.7,8,21 However, biological-network-based methods alone may yield limited insight in cases of insufficient domain knowledge of gene, protein, and metabolite interactions, and are often extended through the incorporation of empirical relationships or correlations between measured species.
[22] Multilevel omics for the discovery of biomarkers and ... - Nature — The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNAs (transcriptomics) and metabolites (metabolomics) can be studied simultaneously
[23] Multi-omics Data Integration - Omics tutorials — Systems Biology Approaches: Integrating metabolomics with other omics layers contributes to systems biology approaches. Systems-level analysis allows for the construction of comprehensive models that capture the interactions between genes, transcripts, proteins, and metabolites, providing a holistic view of cellular functions.
[47] Metabolomics 20 years on: what have we learned and what ... - PubMed — The term metabolome was coined in 1998, by analogy to genome, transcriptome and proteome. The first research papers using the terms metabolomics, metabonomics, metabolic profiling or metabolite profiling were published shortly thereafter. In this short review we reflect on the major achievements bro …
[50] Quantitative metabolomics based on gas chromatography mass spectrometry ... — Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic
[51] Metabolite identification and quantitation in LC-MS/MS-based ... — After combination with effective sample preparation and chromatographic separation, modern QqQ mass spectrometers with faster spectral scan rate (e.g., SRM dwell time of 2 ms in Thermo Quantum TSQ) and higher ionization efficiency (e.g., heated ESI) can achieve simultaneous quantitation of tens to hundreds of metabolites. LC developments, similar to those mentioned previously for SRM-based studies, have been made in LC-FT-MS-based quantitative metabolomics (e.g., utilizing multiple column chemistries in several LC platforms to achieve broad range of metabolites detection), and details are in Table 1. However, it is not widely used for metabolite identification and quantitation because its cost is high, it is hard to maintain and it is difficult to couple with LC, compared with Orbitrap and TOF mass spectrometers (e.g., 15,000 FWHM at m/z 400 and 5–10 ppm for TOF) .
[52] The Potential of Metabolomics in Biomedical Applications — The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development.
[59] Applied metabolomics in drug discovery - PubMed — Applied metabolomics in drug discovery - PubMed doi: 10.1080/17460441.2016.1195365. DOI: 10.1080/17460441.2016.1195365 Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. Areas covered: This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Keywords: Metabolomics; chemical libraries; drug discovery; metabolic network; metabolic profiling; metabonomics; natural products. Metabolomic tools used in marine natural product drug discovery. Application of Combination High-Throughput Phenotypic Screening and Target Identification Methods for the Discovery of Natural Product-Based Combination Drugs.
[70] Current metabolomics: Technological advances - ScienceDirect — Metabolomics is an interdisciplinary study that involves the exhaustive quantitative profiling of metabolites in a target organism using sophisticated analytical technologies. It is a powerful approach that allows researchers to examine variation in total metabolite profiles, and is capable of detecting complex biological changes using
[94] Emerging Biomarkers in Metabolomics: Advancements in Precision ... - MDPI — This review will outline recent advances in biomarker discovery based on metabolomics, focusing on metabolomics biomarkers reported in cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. Keywords: biomarkers; metabolomics; cancer; neurodegenerative; diabetes; gut-microbiota; precision health; disease diagnosis; personalized medicine With advancements in technology and analysis capabilities for metabolomics, it is increasingly possible to identify disease biomarkers even for diseases as complex as cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic syndrome. Yu, E.; Rimm, E.; Qi, L.; Rexrode, K.; Albert, C.M.; Sun, Q.; Willett, W.C.; Hu, F.B.; Manson, J.E. Diet, lifestyle, biomarkers, genetic factors, and risk of cardiovascular disease in the nurses’ health studies. "Emerging Biomarkers in Metabolomics: Advancements in Precision Health and Disease Diagnosis" International Journal of Molecular Sciences 25, no.
[96] Small molecule metabolites: discovery of biomarkers and therapeutic ... — Metabolite biosignatures from human biofluids providing a link between genotype, environment and phenotype, are attractive biomarkers for the clinical diagnosis, prognosis, and diseases classification.1,2,3,4,5,6,7,8 It can provide a unique metabolic readout and snapshot of the health/disease status of key information about the downstream products related to various metabolic processes.9,10,11,12 Differential metabolites can improve the specificity and accuracy as biomarkers for patient diagnosis, patient monitoring, risk prediction and prognosis.13,14,15,16 Discovery and identification of small molecule metabolites or metabolic pathway alterations is useful for understanding the pathophysiology of diseases, and help identify therapeutic targets.17,18,19,20,21,22,23,24,25,26,27 Metabolome represent the upstream input from environment and downstream output of genome, the collection of bioactive small molecule metabolites including nucleotides, carbohydrates, amino acid, and fatty acid, has used for discovery of early prediction and diagnosis biomarkers of diseases that insight into the best use of interventions.28,29,30,31,32,33,34,35 Endogenous metabolites could provide unique metabolic insights into the mechanistic basis and therapeutic targets of disease and also leads to personalized metabolic phenotype.36
[102] Mass-spectrometry-based microbial metabolomics: recent developments and ... — The aim of this review is to present briefly recent technical advances in mass-spectrometry-based analysis, and to highlight the value of modern applications of microbial metabolomics. Metabolomics is an omics technique aiming at qualitatively and quantitatively describing a metabolome by various analytical platforms.
[103] Mass-spectrometry-based microbial metabolomics: recent ... - PubMed — Mass spectrometry and nuclear magnetic resonance spectroscopy techniques are popular analytical strategies prevailing in the metabolomics field. In this review, chromatography-mass-spectrometry-based microbial metabolomic analysis steps are summarized, including sample collection, metabolite extraction, instrument analysis, and data analysis.
[104] Mass spectrometry-based metabolomics in microbiome investigations — Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiome, and to understand the functional role of these microbial metabolites. ... infrared or nuclear magnetic resonance (NMR) spectroscopy can be used. Each of these techniques have pros and cons, the
[106] Mass Spectrometry-Based Microbial Metabolomics: Techniques, Analysis ... — Consequently, metabolomic information is being used to understand microbial metabolic networks. At the forefront of this work is mass spectrometry, the most popular metabolomics measurement technique. Mass spectrometry-based metabolomic analyses have made significant contributions to microbiological research in the environment and human disease.
[108] Current Approaches on Metabolomics - SpringerLink — Analytical techniques, including MS, NMR, FTIR, Raman, and UV-Vis spectroscopy, are discussed in terms of their strengths, limitations, and applications. Emphasis is placed on the importance of meticulous sample preparation, data acquisition, processing, and the role of bioinformatics and computational tools in metabolite identification and
[109] Metabolomics in drug research and development: The ... - ScienceDirect — Metabolomics in drug research and development: The recent advances in technologies and applications - ScienceDirect Metabolomics in drug research and development: The recent advances in technologies and applications With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development. Metabolomics greatly facilitates drug research and development from understanding disease mechanisms and identifying drug targets to predicting drug response and enabling personalized treatment. Next article in issue No articles found. For all open access content, the relevant licensing terms apply.
[110] Current metabolomics: Practical applications - ScienceDirect — The application of metabolomics techniques to plant science was pioneered by groups at the Max Planck Institute. Weckwerth and coworkers obtained a series of metabolome data from potato tubers using GC/MS (32). A pair-wise comparison of metabolite levels showed that many pairs of metabolites exist, and that their levels show high correlation
[111] Metabolomics biotechnology, applications, and future trends: a ... — Metabolomics based on MS is increasingly used in different scientific fields, especially agronomy and plant biology, to understand the behavior of plants under different stress conditions. 87 We have summarized several interesting examples to illustrate the application of metabolomics techniques in agriculture and the biotechnology industry. In
[112] Guide to Metabolomics Analysis: A Bioinformatics Workflow — Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses.
[134] PDF — Gas Chromatography coupled to Mass Spectrometry (GC-MS), Nuclear Mag-netic Resonance spectroscopy (NMR), Direct Infusion Mass Spectrometry (DI-MS), two-dimensional GC coupled to MS (GC GC-MS), Capillary Electrophoresis coupled to MS (CE-MS), Liquid Chromatography coupled to MS (LC-MS), are the main analytical techniques used in metabolomic investigations in the present period (Theodoridis G, et al., 2008; Gika H, et al., 2019; Begou O, et al., 2019).
[138] Analytical platforms in metabolomics of health and disease — Employing high-resolution analytical platforms and chemometric tools, offers a holistic view of metabolism, enhancing our understanding of biological processes and shedding light on how lifestyle and dietary factors impact diseases through low-molecular-weight metabolite measurements .Analytical platforms are crucial in analysing metabolome as it consists of several different types of small
[139] Single Cell Metabolomics: A Path to Biological Diversity — Single Cell Metabolomics: A Path to Biological Diversity - BiologyInsights Explore how single cell metabolomics unveils the complexity of biological diversity through advanced sampling and analytical techniques. Exploring the intricacies of cellular metabolism at the single-cell level provides a unique lens to understand biological diversity. Spatial metabolite profiling offers a transformative approach to understanding complex metabolic landscapes at the single-cell level. For example, a study published in Science demonstrated how single-cell RNA sequencing, combined with metabolomic profiling, can elucidate metabolic pathways supporting stem cell differentiation. Single-cell metabolomics provides profound insights into biological diversity, revealing intricate metabolic variations underlying cellular heterogeneity. Real-world applications of single-cell metabolomics are already emerging, demonstrating its potential to transform our understanding of biological diversity.
[140] Mass Spectrometry in Metabolomics: Advanced LC-MS & GC-MS Techniques ... — Mass spectrometry in metabolomics (LC-MS, GC-MS) is a powerful analytical approach that enables precise biomarker detection, metabolic pathway analysis, and advancements in personalized medicine and drug metabolism. Metabolomics, combined with high-resolution mass spectrometry (HRMS), LC-MS, and GC-MS, enables detailed analysis of metabolic pathways and biomarker discovery for disease diagnostics and drug development. Mass spectrometry for metabolomics, particularly LC-MS and GC-MS techniques, plays a crucial role in biomarker discovery, metabolic profiling, and precision medicine, enabling breakthroughs in medical diagnostics and drug development. With advancements in automation and AI-driven data analysis, mass spectrometry is becoming more accessible for medical diagnostics and drug development, while continuously evolving to offer sophisticated capabilities for analyzing complex biological systems.
[146] Integrating Metabolomics Domain Knowledge with Explainable Machine ... — Machine learning (ML) application in metabolomic analyses is enabling the extraction of meaningful information from complex data. Bringing together domain-specific knowledge from metabolomics with explainable ML methods can refine the predictive performance and interpretability of models used in atherosclerosis research.
[147] Machine Learning Methods for Analysis of Metabolic Data and Metabolic ... — To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric
[148] Machine Learning Methods for Analysis of Metabolic Data and Metabolic ... — Overview of different data analysis steps in metabolomics and metabolism modeling where machine learning methodologies have found uses. Examples include unsupervised and supervised [] type analysis either trying to group samples or features without any user input or bias or trying to get the best classification models based on user-labelled training data, respectively.
[149] An end-to-end deep learning method for mass spectrometry data analysis ... — An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles | Nature Communications We firstly apply this method to differentiate healthy individuals and patients with benign lung nodules or lung adenocarcinoma using 859 serum samples from three distinct hospitals, followed by its extended analysis on lipid metabolomic data derived from 928 cell lines to reveal metabolites and proteins associated with multiple cancer types. To address these issues, DeepMSProfiler directly takes untargeted LC-MS raw data as model input, and builds an end-to-end deep learning model to profile disease-related metabolic signals (Supplementary Fig. 1 bottom).
[168] BiomiX, a user-friendly bioinformatic tool for democratized analysis ... — Various algorithms belong to this family, including matrix factorization–regression and association methods such as Multi-Omics Factor Analysis (MOFA) , Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO) , other matrix factorization methods , IclusterPlus , and network analysis. These include Bayesian networks such as PAthway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) and matrix factorization-based methods such as NEighborhood based Multi-Omics clustering (NEMO) and Similarity Network Fusion (SNF) . BiomiX guarantees high interpretability of the common source of variation among omics, providing users with single results from omic and multiomics integration, in a perspective of its application on single-cell data thanks to the MOFA method .
[169] EasyOmics: A graphical interface for population-scale omics data ... — EasyOmics: A graphical interface for population-scale omics data association, integration and visualization - ScienceDirect EasyOmics: A graphical interface for population-scale omics data association, integration and visualization Here, we present EasyOmics a stand-alone R Shiny application with a user-friendly interface for wet-lab biologists to perform population-scale omics data association, integration and visualization. The toolkit incorporates multiple functions designed to meet the increasing demand for population-scale omics data analyses, ranging from data quality control, heritability estimation, genome-wide association analysis, conditional association analysis, omics quantitative trait locus mapping, omics-wide association analysis, omics data integration and visualization etc. We developed EasyOmics, a user-friendly R Shiny application that enables biologists to perform association analysis, multi-omics integration, and visualization through an intuitive interface without requiring coding expertise. For all open access content, the relevant licensing terms apply.
[172] Metabolomics Research Introduction, Applications, Sample Types and Handling — Metabolomics research is a valuable tool to characterize intra- and inter-cellular, dynamic molecular changes in a multitude of applications such as metabolism of drugs or environmental toxicants and discovery and validation of disease biomarkers.
[173] Metabolomics - an overview | ScienceDirect Topics — Metabolomics is the systematic study of a metabolome, the entirety of metabolites, or a set of metabolites, forming an extensive network of metabolic reactions in which one metabolite from a specific pathway will affect one or more biochemical reactions, or a comprehensive and quantitative analysis of all metabolites (Fiehn, 2001; Oliver et al., 1998). Therefore, metabolomics, as a major part of systems biology, is a far more profound concept and practice than simple metabolic profiling and biomarker search, although comprehensive, qualitative, and quantitative analyses of metabolites are the essential and common procedures in metabolomics studies. Metabolomics is an extension of genomics and proteomics, which studies the changes in all metabolites produced by external stimuli in biological systems (cells, tissues, or organisms).
[180] Metabolomics in pharmacology - a delve into the novel field of ... — Introduction: Pharmacometabolomics is an emerging science pursuing the application of precision medicine.Combining both genetic and environmental factors, the so-called pharmacometabolomic approach guides patient selection and stratification in clinical trials and optimizes personalized drug dosage, improving efficacy and safety.
[181] Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical ... — Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response ... Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most
[193] Metabolomic Profiling for Identification of Novel Biomarkers and ... — In our own work, targeted, MS/MS-based metabolomic profiling has identified novel metabolic biosignatures that predict CVD events independently and incrementally from the known protein biomarkers. 8 In an initial discovery cohort of 314 individuals with CAD, we found that a PCA-derived metabolite factor composed of small-medium chain
[216] Current Challenges and Recent Developments in Mass Spectrometry-Based ... — Here, we discuss the ongoing challenges with MS-based metabolomics, including de novo metabolite identification from mass spectra, differentiation of metabolites from environmental contamination, chromatographic separation of isomers, and incomplete MS databases. Because of their popularity and sensitive detection of small molecules, this
[217] Overview of Mass Spectrometry-Based Metabolomics: Opportunities and ... — While the MS-based metabolomics has provided insights into metabolic pathways and fluxes, and metabolite biomarkers associated with numerous diseases, the increasing realization of the extremely high complexity of biological mixtures underscores numerous challenges including unknown metabolite identification, biomarker validation, and interlaboratory reproducibility that need to be dealt with for realization of the full potential of MS-based metabolomics. Keywords: Mass spectrometry, Ionization methods, Quantitative metabolomics, Mass analyzers, Ambient ionization, MS-imaging, Chromatography, Capillary electrophoresis Because of their high sensitivity and selectivity, TQ and Qtrap analyzers are the most common MS spectrometers hyphenated to LC and employed in targeted metabolic studies, while Q-TOF, LTQ-Orbitrap, and FTICR analyzers are more suitable for global profiling and metabolite identification (including isotopomer analysis) due to their higher mass-resolving power.
[226] PDF — challenge regarding the use of RT for untargeted metabolomics eluting annotation is the high number of closely compounds. In fact, maximizing the number of compounds that can be monitored within a single analyt-ical (run inevitably results in a crowded chromatogram Figure 1). The problem is that RTs in some LC-MS-based
[227] Advanced analytical and informatic strategies for metabolite annotation ... — This 4D identification strategy has been demonstrated to substantially improve the accuracy and coverage of metabolites in biological samples, in particular for isomeric metabolites.
[228] Metabolite Annotation and Identification - ScienceDirect — In this chapter, we describe the different tools and strategies applied to identify metabolites detected in untargeted metabolomics studies that apply mass spectrometry (MS) platforms coupled to various separation techniques: liquid (LC) and gas chromatography (GC) or capillary electrophoresis (CE). Following a standard workflow in metabolite identification, the current limitations are
[233] Machine Learning Applications for Mass Spectrometry-Based Metabolomics ... — Machine Learning Applications for Mass Spectrometry-Based Metabolomics - PubMed Machine Learning Applications for Mass Spectrometry-Based Metabolomics Machine Learning Applications for Mass Spectrometry-Based Metabolomics Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries. Keywords: MS-based metabolomics; machine learning; metabolic engineering; metabolic flux analysis; multi-omics; synthetic biology. Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis. A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification. Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data. Saccenti E., Hoefsloot H.C.J., Smilde A.K., Westerhuis J.A., Hendriks M.M.W.B. Reflections on univariate and multivariate analysis of metabolomics data.
[239] Deep metabolome: Applications of deep learning in metabolomics — Deep metabolome: Applications of deep learning in metabolomics - ScienceDirect Deep learning has been most widely applied in data pre-processing step. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes. Deep Neural Network For all open access content, the relevant licensing terms apply.
[240] Frontiers | Applications of machine learning in metabolomics: Disease ... — The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. ... PubMed was searched using the keywords "metabolomics" and "machine learning
[241] Integrating Machine Learning in Metabolomics: A Path to Enhanced ... — Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation - PubMed Search: Search Your saved search Name of saved search: Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.
[242] Precision Medicine Approaches with Metabolomics and Artificial ... — This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis.
[243] Synergizing metabolomics and artificial intelligence for advancing ... — Metabolomics has emerged as a transformative tool in precision oncology, with substantial potential for advancing biomarker discovery, monitoring treatment responses, and aiding drug development. Integrating artificial intelligence (AI) into metabolomics optimizes data acquisition and analysis, faci …
[251] Advanced analytical and informatic strategies for metabolite annotation ... — In this review, we focus on discussing the major advancements of metabolite annotation strategies in LC-MS-based untargeted metabolomics, which include the tandem mass spectral match, in-silico MS/MS spectral prediction, and network-based approaches. Further, we review and discuss the expansion of analytical dimensions to support multidimensional metabolite annotation with a focus on liquid
[252] Good practices and recommendations for using and benchmarking ... — Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics
[255] Recent advances in metabolomics analysis for early drug development — Recent advances in metabolomics analysis for early drug development - ScienceDirect Recent advances in metabolomics analysis for early drug development Metabolomics has become a widely applied tool in drug development. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development. Next article in issue For all open access content, the relevant licensing terms apply.
[256] Emerging Biomarkers in Metabolomics: Advancements in Precision Health ... — Emerging Biomarkers in Metabolomics: Advancements in Precision Health and Disease Diagnosis - PMC This review will outline recent advances in biomarker discovery based on metabolomics, focusing on metabolomics biomarkers reported in cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. Keywords: biomarkers, metabolomics, cancer, neurodegenerative, diabetes, gut-microbiota, precision health, disease diagnosis, personalized medicine Furthermore, the biomarkers through metabolomics allow the facilitation of early diagnosis, treatment response, and disease surveillance of cancers to be further advanced toward personalized and precision oncology . 135.Yu E., Rimm E., Qi L., Rexrode K., Albert C.M., Sun Q., Willett W.C., Hu F.B., Manson J.E. Diet, lifestyle, biomarkers, genetic factors, and risk of cardiovascular disease in the nurses’ health studies.
[257] Recent advances in metabolomics analysis for early drug ... - PubMed — The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few d …
[258] Harnessing the Power of Metabolomics for Precision Oncology: Current ... — Future Directions. Metabolomics has shown extensive applications for diagnosing and treating tumours but it is most often used in isolation and is not always integrated with other profiling modalities, such as spatial omics or proteomics. Further advances in metabolomics and computational techniques will allow for a more holistic understanding
[261] Metabolomics and lipidomics strategies in modern drug discovery and ... — We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers. Alterations in metabolite levels are intricately tied to diverse diseases, revealing insights into disease etiology, status, therapeutic response, and biological process changes.4 The systematic study of metabolites found within biological systems is termed ‘metabolomics’.5, 6 Whereas metabolomics aims to identify and quantify the composition of metabolites, lipidomics is a subset of metabolomics that focuses specifically on lipid metabolites. We explore the diverse applications of metabolomics across various stages of drug discovery and development, encompassing foundational research, target identification, mechanism-of-action studies, and the creation of clinically applicable biomarkers.
[263] The Role of Metabolomics in Drug Development and Toxicology — The integration of metabolomics with genomics, transcriptomics, and proteomics has created a holistic approach to drug development and toxicology. This multi-omics strategy allows researchers to explore complex biological networks and gain a comprehensive understanding of disease mechanisms and drug responses.
[266] Synergizing metabolomics and artificial intelligence for advancing ... — Metabolomics has emerged as a transformative tool in precision oncology, with substantial potential for advancing biomarker discovery, monitoring treatment responses, and aiding drug development. Integrating artificial intelligence (AI) into metabolomics optimizes data acquisition and analysis, facilitating the interpretation of complex metabolic networks and enabling more effective multiomics
[268] Precision Medicine Approaches with Metabolomics and Artificial ... — This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. Keywords: artificial intelligence; biomarkers; machine learning; metabolomics; precision medicine. Machine learning model training and prediction of new sample using metabolomics analysis of… Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine. Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches. Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery. - DOI - PMC - PubMed - DOI - PMC - PubMed - DOI - PMC - PubMed - DOI - PMC - PubMed
[269] Artificial intelligence in metabolomics: a current review — In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. In this article, we present a systematic review of AI techniques and applications in metabolomic studies in the framework of systems biology. We then present a comprehensive review of AI applications across different aspects of metabolomics studies, highlighting the unique characteristics of metabolomics data and important considerations for effective analysis and appropriate interpretation. The integration of AI in metabolomics has greatly improved data analysis, biological interpretation, and predictive statistical modeling .
[270] AI in Multi-Omics: Integrating Metabolomics, Proteomics, Genomics — AI In Multi-Omics: Integrating Metabolomics, Proteomics, Genomics AI in Multi-Omics: Integrating Metabolomics, Proteomics, Genomics How AI Revolutionizes Multi-Omics AI Techniques for Multi-Omics Integration For example, in diabetes care, AI analyzes multi-omics data to predict insulin response, allowing personalized dosing strategies. A notable example is AI-driven research into Alzheimer’s disease, where AI pinpointed specific proteins for therapeutic targeting based on multi-omics analysis. How reliable is AI-driven multi-omics research? Robust AI tools like DeepOmics have demonstrated high accuracy, such as predicting patient survival rates in cancer trials by integrating multi-omics data. AI-integrated multi-omics is revolutionizing environmental studies. Can AI improve healthcare access through multi-omics? Yes, AI enables cost-effective diagnostics by analyzing multi-omics data for early disease detection. Nature Biotechnology: Publishes cutting-edge research on multi-omics integration and AI applications in biology.
[271] Integration of Omics and Phenotypic Data for Precision Medicine — The integration of individual's biology data (e.g., genomics, proteomics, metabolomics), and health-care data has created unprecedented opportunities for precision medicine, that is, a medical model that uses a patient's unique information, mainly genetic, to prevent, diagnose, or treat disease.
[273] Genomic, Proteomic, and Metabolomic Data Integration Strategies — This article focuses on select methods and tools for the integration of metabolomic with genomic and proteomic data. Metabolomics, the analysis of small molecules (eg, 1200 Da) and biochemical intermediates (metabolites), has been widely used to study interactions between gene and protein downstream products and environmental stimuli.
[274] AI in Multi-Omics: Integrating Metabolomics, Proteomics, Genomics — AI In Multi-Omics: Integrating Metabolomics, Proteomics, Genomics AI in Multi-Omics: Integrating Metabolomics, Proteomics, Genomics How AI Revolutionizes Multi-Omics AI Techniques for Multi-Omics Integration For example, in diabetes care, AI analyzes multi-omics data to predict insulin response, allowing personalized dosing strategies. A notable example is AI-driven research into Alzheimer’s disease, where AI pinpointed specific proteins for therapeutic targeting based on multi-omics analysis. How reliable is AI-driven multi-omics research? Robust AI tools like DeepOmics have demonstrated high accuracy, such as predicting patient survival rates in cancer trials by integrating multi-omics data. AI-integrated multi-omics is revolutionizing environmental studies. Can AI improve healthcare access through multi-omics? Yes, AI enables cost-effective diagnostics by analyzing multi-omics data for early disease detection. Nature Biotechnology: Publishes cutting-edge research on multi-omics integration and AI applications in biology.
[275] Synergizing metabolomics and artificial intelligence for advancing ... — Metabolomics has emerged as a transformative tool in precision oncology, with substantial potential for advancing biomarker discovery, monitoring treatment responses, and aiding drug development. Integrating artificial intelligence (AI) into metabolomics optimizes data acquisition and analysis, facilitating the interpretation of complex metabolic networks and enabling more effective multiomics
[286] Metabolomics in Systems Biology - PubMed — These high-throughput instruments play an extremely crucial role in discovery metabolomics to generate data needed for further analysis. In this chapter, the concept of metabolomics in the context of systems biology is discussed and provides examples of its application in human disease studies, plant responses towards stress and abiotic