Shuzhao Li

The paper analyzes metabolomics 'dark matter' using a detectable khipu model, showing most abundant features have identifiable ion patterns, while many remain unknown.

The paper profiles skin responses to 187 Staphylococcus strains across seven species in a human skin model, revealing strain-level diversity in host immune and barrier responses.

The paper introduces a circular van Krevelen diagram for automated metabolic pathway visualization, improving consistency for data interpretation and map creation.

The paper constructs a consensus serum metabolome from over 100,000 mass spectrometry acquisitions, revealing gaps in current databases and improving LC-MS metabolomics annotation.

The paper proposes identification probability as an automated and transferable method to assess metabolite identification confidence in metabolomics.

The paper introduces a Python pipeline and data structures for metabolomics data processing, annotation, and standardization, demonstrating its utility with large-scale datasets.

This paper introduces a generalized tree structure and algorithm to annotate ions in metabolomics data and infer neutral mass, implemented as a Python package called khipu.