Wen‐Feng Zeng

1.9k total citations
23 papers, 879 citations indexed

About

Wen‐Feng Zeng is a scholar working on Spectroscopy, Molecular Biology and Biomedical Engineering. According to data from OpenAlex, Wen‐Feng Zeng has authored 23 papers receiving a total of 879 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Spectroscopy, 18 papers in Molecular Biology and 2 papers in Biomedical Engineering. Recurrent topics in Wen‐Feng Zeng's work include Advanced Proteomics Techniques and Applications (17 papers), Mass Spectrometry Techniques and Applications (13 papers) and Machine Learning in Bioinformatics (5 papers). Wen‐Feng Zeng is often cited by papers focused on Advanced Proteomics Techniques and Applications (17 papers), Mass Spectrometry Techniques and Applications (13 papers) and Machine Learning in Bioinformatics (5 papers). Wen‐Feng Zeng collaborates with scholars based in China, Germany and Denmark. Wen‐Feng Zeng's co-authors include Si‐Min He, Xie‐Xuan Zhou, Hao Chi, Weiqian Cao, Mingqi Liu, Pengyuan Yang, Jianfeng Zhan, Chunjie Luo, Chao Liu and Matthias Mann and has published in prestigious journals such as Nature Communications, Nature Biotechnology and Analytical Chemistry.

In The Last Decade

Wen‐Feng Zeng

22 papers receiving 865 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Wen‐Feng Zeng China 14 732 513 53 51 51 23 879
Wen Yu United States 15 751 1.0× 887 1.7× 35 0.7× 34 0.7× 75 1.5× 19 1.3k
Christian M. Beusch Sweden 10 355 0.5× 139 0.3× 35 0.7× 24 0.5× 41 0.8× 20 495
Klaus Rumpel United Kingdom 15 655 0.9× 237 0.5× 45 0.8× 53 1.0× 46 0.9× 24 884
Michael W. Belford United States 13 654 0.9× 817 1.6× 14 0.3× 26 0.5× 31 0.6× 20 1.1k
Dorothee Childs Germany 6 565 0.8× 247 0.5× 65 1.2× 31 0.6× 30 0.6× 6 767
Jason M. Hogan United States 17 733 1.0× 903 1.8× 50 0.9× 93 1.8× 80 1.6× 30 1.3k
Yan Ting Lim Singapore 14 390 0.5× 146 0.3× 26 0.5× 42 0.8× 73 1.4× 20 634
Dain R. Brademan United States 12 455 0.6× 335 0.7× 14 0.3× 30 0.6× 24 0.5× 15 625
Elena A. Ponomarenko Russia 16 638 0.9× 268 0.5× 12 0.2× 23 0.5× 24 0.5× 82 826
Philip M. Remes United States 11 702 1.0× 779 1.5× 17 0.3× 21 0.4× 33 0.6× 21 1.1k

Countries citing papers authored by Wen‐Feng Zeng

Since Specialization
Citations

This map shows the geographic impact of Wen‐Feng Zeng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wen‐Feng Zeng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wen‐Feng Zeng more than expected).

Fields of papers citing papers by Wen‐Feng Zeng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wen‐Feng Zeng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wen‐Feng Zeng. The network helps show where Wen‐Feng Zeng may publish in the future.

Co-authorship network of co-authors of Wen‐Feng Zeng

This figure shows the co-authorship network connecting the top 25 collaborators of Wen‐Feng Zeng. A scholar is included among the top collaborators of Wen‐Feng Zeng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wen‐Feng Zeng. Wen‐Feng Zeng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wallmann, Georg, Patricia Skowronek, Marvin Thielert, et al.. (2025). AlphaDIA enables DIA transfer learning for feature-free proteomics. Nature Biotechnology. 1 indexed citations
2.
Yuan, Y., et al.. (2025). DeepSecMS Advances DIA‐Based Selenoproteome Profiling Through Cys‐to‐Sec Proxy Training. Advanced Science. 12(38). e04109–e04109. 1 indexed citations
3.
Wen, Bo, Chris Hsu, David Shteynberg, et al.. (2025). Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics. Nature Communications. 16(1). 9815–9815.
4.
Steigerwald, Sophia, Ankit Sinha, Kyle L. Fort, et al.. (2024). Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis. Molecular & Cellular Proteomics. 23(2). 100713–100713. 3 indexed citations
5.
Bogdanow, Boris, et al.. (2024). Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies. Molecular Systems Biology. 21(1). 90–106. 1 indexed citations
6.
Zeng, Wen‐Feng, Guoquan Yan, Huanhuan Zhao, Chao Liu, & Weiqian Cao. (2024). Uncovering missing glycans and unexpected fragments with pGlycoNovo for site-specific glycosylation analysis across species. Nature Communications. 15(1). 8055–8055. 7 indexed citations
7.
Strauss, Maximilian T., Isabell Bludau, Wen‐Feng Zeng, et al.. (2024). AlphaPept: a modern and open framework for MS-based proteomics. Nature Communications. 15(1). 2168–2168. 21 indexed citations
8.
Schweizer, Lisa, Tina Schaller, Maximilian Zwiebel, et al.. (2023). Quantitative multiorgan proteomics of fatal COVID‐19 uncovers tissue‐specific effects beyond inflammation. EMBO Molecular Medicine. 15(9). e17459–e17459. 13 indexed citations
9.
Thielert, Marvin, Constantin Ammar, Florian A. Rosenberger, et al.. (2023). Robust dimethyl‐based multiplex‐DIA doubles single‐cell proteome depth via a reference channel. Molecular Systems Biology. 19(9). e11503–e11503. 47 indexed citations
10.
Wahle, Maria, Marvin Thielert, Maximilian Zwiebel, et al.. (2023). IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma. Molecular & Cellular Proteomics. 23(1). 100689–100689. 10 indexed citations
11.
Zeng, Wen‐Feng, Xie‐Xuan Zhou, Sander Willems, et al.. (2022). AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nature Communications. 13(1). 7238–7238. 101 indexed citations
12.
Kong, Siyuan, Wen‐Feng Zeng, Biyun Jiang, et al.. (2022). pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level. Nature Communications. 13(1). 7539–7539. 33 indexed citations
13.
Zeng, Wen‐Feng, et al.. (2021). pDeep3: Toward More Accurate Spectrum Prediction with Fast Few-Shot Learning. Analytical Chemistry. 93(14). 5815–5822. 32 indexed citations
14.
Zeng, Wen‐Feng, Weiqian Cao, Mingqi Liu, Si‐Min He, & Pengyuan Yang. (2021). Precise, fast and comprehensive analysis of intact glycopeptides and modified glycans with pGlyco3. Nature Methods. 18(12). 1515–1523. 135 indexed citations
15.
Chen, Zhen-Lin, et al.. (2021). pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning. Journal of Proteome Research. 20(5). 2570–2582. 14 indexed citations
16.
Jiang, Wen, Bo Wen, Kai Li, et al.. (2021). Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification. Molecular & Cellular Proteomics. 20. 100171–100171. 7 indexed citations
17.
Wen, Bo, Wen‐Feng Zeng, Yuxing Liao, et al.. (2020). Deep Learning in Proteomics. PROTEOMICS. 20(21-22). e1900335–e1900335. 101 indexed citations
18.
Zhou, Wenjing, et al.. (2019). pValid: Validation Beyond the Target-Decoy Approach for Peptide Identification in Shotgun Proteomics. Journal of Proteome Research. 18(7). 2747–2758. 14 indexed citations
19.
Yang, Hao, Hao Chi, Wenjing Zhou, et al.. (2017). pSite: Amino Acid Confidence Evaluation for Quality Control of De Novo Peptide Sequencing and Modification Site Localization. Journal of Proteome Research. 17(1). 119–128. 15 indexed citations
20.
Zeng, Wen‐Feng, Mingqi Liu, Yang Zhang, et al.. (2016). pGlyco: a pipeline for the identification of intact N-glycopeptides by using HCD- and CID-MS/MS and MS3. Scientific Reports. 6(1). 25102–25102. 80 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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