Metabolomics Data

Metabolomics data analysis focuses on identifying and quantifying small molecules (metabolites) within biological samples to understand metabolic processes and their relation to health and disease. Current research emphasizes the integration of metabolomics with other omics data (e.g., genomics, transcriptomics) and the application of machine learning techniques, including deep learning (e.g., variational autoencoders, graph neural networks, and ensemble methods like XGBoost and Random Forests), to improve data imputation, classification, and biomarker discovery. These advancements are significantly impacting various fields, enabling more accurate disease diagnosis, personalized medicine approaches, and a deeper understanding of complex biological systems.