Heejung Yang

The paper introduces microbiomeMASST, a metadata network graph linking metabolites across datasets, enabling cross-study analysis of microbial metabolites and their biological context.

This paper introduces a deep learning model for generating spectral libraries in DIA-MS by incorporating physicochemical features and sparsity regularization, outperforming existing models in retention time prediction and peptide identification.

The paper curates LC-MS/MS data from plant extracts to create a taxonomically focused database for exploring plant-derived molecules.

The paper creates a suspect spectral library for untargeted metabolomics by propagating annotations from public MS/MS spectra to improve interpretation of unannotated spectra.

This study uses a multipartite network approach to analyze interactions between medicinal plant chemicals and targets, identifying clusters and potential drug candidates.

ChexMix extracts bioentity identifiers from literature to construct taxonomic trees and identify co-occurring medical subject headings.

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.