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Combining genome-wide association studies (GWAS) with metabolomics and Mendelian randomization is transforming precision medicine by uncovering causal links between genetic variants and clinical outcomes, rather than just correlations. This approach deepens GWAS insights, helping identify actionable therapeutic targets, yet remains underutilized in research.
Data interpretation is often overlooked in omics training. But without knowing how to make sense of our results in the broader biological context, we’ll struggle to pull out actionable insights from our data.The biological interpretation of metabolomics is a long but...
While there’s no single correct approach to figuring out your sample matrix, there certainly are things that could be done wrong. Here, we dive into some of the advantages and advantages of the sample matrices you might consider for you metabolomics study.
Pre-analytics influences metabolite concentrations. Adherence to standardized sample collection and storage protocols is crucial for reliable metabolomics data
Metabolomics can be combined with clinical data for the prediction of patient survival after anti-tumor treatment.
