Metabolomics is a science that studies the abundance and composition changes of small molecule metabolites in organisms. The pathophysiological changes of diseases are often associated with changes in metabolic pathways. The current metabolomics research can be roughly divided into the following two categories according to their research objectives: (1) studies on the mechanism and significance of metabolic pathway changes to find new therapeutic targets. That is metabolic reprogramming or metabolic remodeling. (2) look for molecular markers or metabolomics models for disease diagnosis, efficacy evaluation, recurrence, and prognosis in blood, body fluid, excreta, and other samples. In both cases, appropriate data analysis methods need to be used to process the large amount of data generated by metabolomics to find suitable research objects, namely small molecule metabolites.
At present, the commonly used metabolomics detection platforms include gas chromatograph-mass spectrometer (GC-MS), liquid chromatography-mass spectrometry (LC-MS), capillary electrophoresis time of flight mass spectrometry (CE-TOF/MS), and nuclear magnetic resonance (NMR). A multivariate data set is obtained by processing the original data with computer software, such as extraction/peak discrimination, peak alignment, and normalization, and then the multivariate data set is mined to find useful information. This kind of data is very large, and it is difficult to obtain meaningful information by conventional data processing methods. In recent years, various big data processing methods have been used in metabolomics and achieved fruitful results, especially pattern recognition. Through this Research Topic, we hope to present more of the latest big data processing and pattern recognition methods to be used in the metabonomic analysis, to obtain innovative molecular targets, biomarkers, various innovative metabonomic diagnoses, and prognosis models. The goal is to provide new methods for disease prevention and control.
We focused on analyzing metabolomic data using big data processing methods, to provide new targets for the study of metabolic pathogenesis of diseases and to find new molecular markers for the diagnosis, prognosis, and efficacy evaluation of diseases. We consider Original Research, Mini-Review, and Review articles on, but not limited to:
• Metabolic reprogram of diseases
• Pattern recognition and other novel big data processing methods application in diseases metabolomics study
• Joint study of multi-omics including metabolomics
• Discovery and validation of novel disease metabolic reprogram markers
• Comparison of advantages and disadvantages of different data processing methods in diseases metabolomics
Metabolomics is a science that studies the abundance and composition changes of small molecule metabolites in organisms. The pathophysiological changes of diseases are often associated with changes in metabolic pathways. The current metabolomics research can be roughly divided into the following two categories according to their research objectives: (1) studies on the mechanism and significance of metabolic pathway changes to find new therapeutic targets. That is metabolic reprogramming or metabolic remodeling. (2) look for molecular markers or metabolomics models for disease diagnosis, efficacy evaluation, recurrence, and prognosis in blood, body fluid, excreta, and other samples. In both cases, appropriate data analysis methods need to be used to process the large amount of data generated by metabolomics to find suitable research objects, namely small molecule metabolites.
At present, the commonly used metabolomics detection platforms include gas chromatograph-mass spectrometer (GC-MS), liquid chromatography-mass spectrometry (LC-MS), capillary electrophoresis time of flight mass spectrometry (CE-TOF/MS), and nuclear magnetic resonance (NMR). A multivariate data set is obtained by processing the original data with computer software, such as extraction/peak discrimination, peak alignment, and normalization, and then the multivariate data set is mined to find useful information. This kind of data is very large, and it is difficult to obtain meaningful information by conventional data processing methods. In recent years, various big data processing methods have been used in metabolomics and achieved fruitful results, especially pattern recognition. Through this Research Topic, we hope to present more of the latest big data processing and pattern recognition methods to be used in the metabonomic analysis, to obtain innovative molecular targets, biomarkers, various innovative metabonomic diagnoses, and prognosis models. The goal is to provide new methods for disease prevention and control.
We focused on analyzing metabolomic data using big data processing methods, to provide new targets for the study of metabolic pathogenesis of diseases and to find new molecular markers for the diagnosis, prognosis, and efficacy evaluation of diseases. We consider Original Research, Mini-Review, and Review articles on, but not limited to:
• Metabolic reprogram of diseases
• Pattern recognition and other novel big data processing methods application in diseases metabolomics study
• Joint study of multi-omics including metabolomics
• Discovery and validation of novel disease metabolic reprogram markers
• Comparison of advantages and disadvantages of different data processing methods in diseases metabolomics
