Wen-Feng Zeng

发表

pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning.

Hao Chi, Si-Min He, Zhen-Lin Chen, 2021, Journal of proteome research.

pNovo+: de novo peptide sequencing using complementary HCD and ETD tandem mass spectra.

M. Dong, Hao Chi, Hai-Feng Chen, 2013, Journal of proteome research.

pTop 1.0: A High-Accuracy and High-Efficiency Search Engine for Intact Protein Identification.

Hao Chi, Chao Liu, Si-Min He, 2016, Analytical chemistry.

pSite: Amino Acid Confidence Evaluation for Quality Control of De Novo Peptide Sequencing and Modification Site Localization.

Hao Chi, Chao Liu, Si-Min He, 2018, Journal of proteome research.

pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

Chunjie Luo, Hao Chi, Chao Liu, 2017, Analytical chemistry.

pQuant improves quantitation by keeping out interfering signals and evaluating the accuracy of calculated ratios.

Hao Chi, Chao Liu, Si-Min He, 2014, Analytical chemistry.

A theoretical foundation of the target-decoy search strategy for false discovery rate control in proteomics

Chao Liu, Si-Min He, Lan Luo, 2015, 1501.00537.

In-depth mapping of the mouse brain N-glycoproteome reveals widespread N-glycosylation of diverse brain proteins

Peng-Yuan Yang, Lei Zhang, Jun Yao, 2016, Oncotarget.

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework

Hao Yang, Hao Chi, Si-Min He, 2019, Bioinform..

Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine

Chao Liu, Hao Yang, Tao Liu, 2018, Nature Biotechnology.

The structural context of posttranslational modifications at a proteome-wide scale

Maximilian T. Strauss, B. Schulman, S. Willems, 2022, PLoS biology.

AlphaViz: Visualization and validation of critical proteomics data directly at the raw data level

Maximilian T. Strauss, A. Brunner, S. Willems, 2022, bioRxiv.

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics

S. Willems, Wen-Feng Zeng, Eugenia Voytik, 2022, bioRxiv.

pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification

Hao Chi, Chao Liu, Si-Min He, 2017, Nature Communications.

pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification

M. Dong, Hao Chi, Si-Min He, 2017, Nature Communications.

MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning.

Hao Chi, Si-Min He, Wen-Feng Zeng, 2019, Analytical chemistry.

Artificial intelligence for proteomics and biomarker discovery.

Maximilian T. Strauss, Chanchal Kumar, Wen-Feng Zeng, 2021, Cell systems.

Deep Learning in Proteomics

Bing Zhang, Wen Jiang, Bo Wen, 2020, Proteomics.

pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level

Wen-Feng Zeng, Weiqian Cao, G. Yan, 2022, Nature Communications.

pGlycoQuant with a deep residual network for precise and minuscule-missing-value quantitative glycoproteomics enabling the functional exploration of site-specific glycosylation

Peng-Yuan Yang, Wen-Feng Zeng, Weiqian Cao, 2021, bioRxiv.

A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides

Hao Chi, Chao Liu, Si-Min He, 2019, Nature Communications.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

A. Nesvizhskii, V. Petyuk, Bo Wen, 2021, Molecular & cellular proteomics : MCP.

Robust dimethyl-based multiplex-DIA workflow doubles single-cell proteome depth via a reference channel

Wen-Feng Zeng, F. Theis, Isabell Bludau, 2022, bioRxiv.

Open-pNovo: De Novo Peptide Sequencing with Thousands of Protein Modifications.

Hao Chi, Chao Liu, Si-Min He, 2017, Journal of proteome research.

AlphaPept, a modern and open framework for MS-based proteomics

Maximilian T. Strauss, F. Meier, S. Willems, 2021, bioRxiv.

Precise, fast and comprehensive analysis of intact glycopeptides and modified glycans with pGlyco3

Si-Min He, Wen-Feng Zeng, Weiqian Cao, 2021, Nature Methods.

Open-pFind enables precise, comprehensive and rapid peptide identification in shotgun proteomics

Yao Zhang, Si-Min He, Long Wu, 2018, bioRxiv.

A note on the false discovery rate of novel peptides in proteogenomics

Chao Liu, Kun Zhang, Yuan Gao, 2015, Bioinform..

Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics*

C. Overall, Hao Chi, Si-Min He, 2019, Molecular & Cellular Proteomics.

pDeep3: Toward More Accurate Spectrum Prediction with Fast Few-Shot Learning.

Wen-Feng Zeng, Ching Tarn, Wen-feng Zeng, 2021, Analytical chemistry.

pGlyco: a pipeline for the identification of intact N-glycopeptides by using HCD- and CID-MS/MS and MS3

Hao Chi, Yang Zhang, Si-Min He, 2016, Scientific Reports.

A Deep Learning‐Based Tumor Classifier Directly Using MS Raw Data

Cheng Chang, Kunxian Shu, Yunping Zhu, 2020, Proteomics.