代谢组学

Metabolomics studies the comprehensive metabolic fingerprint of an organism, providing insights into its biological functions and responses to environmental factors.
代谢组学研究生物体的全面代谢图谱，揭示其生物学功能和对环境因素的响应。
The primary goal of untargeted metabolomics is to identify and quantify all metabolites present in a biological sample without prior knowledge.
无目标代谢组学的主要目标是在没有预先知识的情况下，鉴定并量化生物样本中存在的所有代谢物。
Metabolic pathways can be elucidated through comparative metabolomics analysis between healthy and diseased individuals.
通过健康与疾病个体的比较代谢组学分析，可以阐明代谢途径。
Metabolomics has proven useful in understanding the role of gut microbiota in human health and disease.
代谢组学在理解肠道微生物群在人类健康和疾病中的作用方面发挥了重要作用。
Metabolomic profiling helps researchers track changes in cellular metabolism during different stages of development or in response to treatments.
代谢组学成像有助于研究人员追踪细胞代谢在发育不同阶段或治疗反应中的变化。
High-throughput metabolomics techniques enable rapid and extensive analysis of biological samples for biomarker discovery.
高通量代谢组学技术允许快速广泛地分析生物样本以发现生物标志物。
Integration of multi-omics data, including metabolomics, genomics, and proteomics, provides a holistic view of cellular function.
多组学数据（包括代谢组学、基因组学和蛋白质组学）的整合提供了细胞功能的整体视角。
Metabolomics studies have shed light on the connection between nutrition and chronic diseases like diabetes and obesity.
代谢组学研究揭示了营养与糖尿病和肥胖等慢性疾病之间的联系。
Metabolite set enrichment analysis (MSEA) is a popular statistical method used to identify significant metabolic pathways in metabolomics data.
代谢物集富集分析（MSEA）是一种流行统计方法，用于在代谢组数据中识别重要的代谢途径。
The development of advanced bioinformatics tools is crucial for the interpretation and visualization of complex metabolomics data.
先进的生物信息学工具的发展对于理解和可视化复杂的代谢组数据至关重要。