Xi Xing

The paper uses in vivo isotope tracing and AI to identify biosynthetic origins of unknown metabolites, revealing new metabolic pathways and age-related changes.

This study maps spatial metabolic gradients in murine liver and intestine using integrated experimental-computational methods, revealing distinct metabolic patterns and fructose-induced metabolic derangements.

This paper introduces DeepMet, a chemical language model that predicts and discovers unknown metabolites using mass spectrometry data.

The paper quantifies nutrient preferences of gut bacteria in mice using isotope tracing and finds dietary fiber, protein, and host-derived metabolites drive microbial growth.

The paper introduces NetID, a global network optimization method for annotating untargeted metabolomics data, identifying novel metabolites in yeast and mouse datasets.