Mapping Metabolic Signatures with Machine Learning and Deep Learning

Abstract The use of machine learning and artificial intelligence in analyzing evolving metabolite profiles can provide valuable insights into understanding health, aging patterns, and disease processes in complex biological systems. These metabolic signatures can be used to identify the impact of lifestyle changes and therapeutics on organ function and well-being. This article discusses the major metabolomics toolkits that are being developed to collect and analyze these datasets. These toolkits are crucial to understanding the downstream effects of changes in proteomics and genomics. Applying machine learning and artificial intelligence to metabolomics can revolutionize the field and provide new insights into understanding biological systems and disease processes. In addition, introducing new computing and analysis techniques can further automate the data analysis process, reducing the need for manual data analysis, increasing speed and accuracy and prompt new insights into metabolic pathways and biomarkers. This, in turn, will lead to developing new diagnostic and therapeutic disease strategies. This article serves as a resource for researchers and practitioners in the field, highlighting the importance and potential of artificial intelligence in metabolomics data analysis.