Yong Zang

DiCE identifies disease-associated genes by combining differential expression with network centrality analysis, validated on prostate cancer datasets to find genes correlated with disease-free survival.

TopDIA is a software tool for proteoform identification using TD-DIA-MS, showing improved identification compared to TD-DDA-MS in E. coli.

The paper identifies a novel marker gene set, CDHu40, for neuroendocrine prostate cancer (NEPC) using gene expression and PPI network analysis, showing strong prognostic significance.

TopFD is a tool for top-down proteomics that improves proteoform feature detection accuracy and reproducibility compared to existing methods.

The paper introduces PTM-TBA, a software pipeline that identifies and localizes post-translational modifications in proteoforms using top-down and bottom-up mass spectrometry combined with UniProt annotations.

scFLUX predicts single-cell metabolic fluxome and variations using transcriptomics data, offering a web server and local tools for individual cell or sample-level analysis.

The paper evaluates machine learning models for predicting proteoform retention and migration times in top-down mass spectrometry, finding high accuracy with Prosit and a neural network model.

This paper introduces scFEA, a graph neural network model to estimate cell-wise metabolic flux from scRNA-seq data, validated with matched metabolomics data and applied to multiple datasets.

SSMD improves cell type identification and deconvolution in mouse transcriptomics by addressing variations in genetics, experimental platforms, and small sample sizes.

ICTD improves tissue microenvironment analysis by enhancing cell type identification, marker gene discovery, and functional inference through a semi-supervised deconvolution approach.

The paper introduces LTMG, a model for capturing transcriptional regulatory heterogeneities in scRNA-seq data, showing improved fitting and validation on multiple datasets.