Fangfei Zhang

The paper introduces a database called COVIDpro that curates proteomic data from COVID-19 patients and provides tools for analyzing protein dysregulation to identify biomarkers and therapeutic targets.

DPHL v2 improves upon DPHL v1 by including semi-tryptic peptides and protein isoforms, increasing identified and dysregulated proteins in colorectal cancer analysis.

Dear-DIAXMBD is a deep autoencoder method for analyzing DIA proteomics data, achieving superior performance across different species and instrument platforms.

The paper constructs a comprehensive mass spectral library for human thyroid tissues to improve the detection of malignant nodules through proteomic analysis.

Alpha-Frag improves peptide identification by predicting fragment ion presence with a deep neural network, enhancing detection rates in DDA and DIA data.

BatchServer provides a web platform for evaluating, visualizing, and correcting batch effects in omics data using autoComBat, PVCA, and UMAP.

This paper presents a computational strategy to optimize spectral library size for DIA-MS data, improving peptide and protein identification rates in colorectal and carcinoma samples.

This paper develops a deep learning method using DIA tensor for phenotype prediction from mass spectrometry data, achieving high accuracy in cancer classification.