Chengpin Shen

828 total citations
23 papers, 570 citations indexed

About

Chengpin Shen is a scholar working on Molecular Biology, Spectroscopy and Oncology. According to data from OpenAlex, Chengpin Shen has authored 23 papers receiving a total of 570 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 12 papers in Spectroscopy and 2 papers in Oncology. Recurrent topics in Chengpin Shen's work include Advanced Proteomics Techniques and Applications (12 papers), Metabolomics and Mass Spectrometry Studies (9 papers) and Mass Spectrometry Techniques and Applications (6 papers). Chengpin Shen is often cited by papers focused on Advanced Proteomics Techniques and Applications (12 papers), Metabolomics and Mass Spectrometry Studies (9 papers) and Mass Spectrometry Techniques and Applications (6 papers). Chengpin Shen collaborates with scholars based in China, Australia and United States. Chengpin Shen's co-authors include Liang Qiao, Yi Yang, Pengyuan Yang, Yu Lin, Xiaohui Liu, Ying He, Yi Jia, Anmei Deng, Shuping Long and Qin Qin and has published in prestigious journals such as Nature Communications, Analytical Chemistry and ACS Applied Materials & Interfaces.

In The Last Decade

Chengpin Shen

22 papers receiving 563 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chengpin Shen China 11 361 209 91 82 40 23 570
Elisabeth O. Hochleitner Germany 10 349 1.0× 94 0.4× 18 0.2× 127 1.5× 66 1.6× 14 597
Ana G. Pereira‐Medrano United Kingdom 5 213 0.6× 113 0.5× 34 0.4× 21 0.3× 9 0.2× 7 328
Adam L. Meadows United States 8 296 0.8× 18 0.1× 120 1.3× 34 0.4× 17 0.4× 9 454
Yaling Li China 14 277 0.8× 17 0.1× 42 0.5× 61 0.7× 48 1.2× 58 604
Jessica De Ingeniis United States 10 420 1.2× 9 0.0× 56 0.6× 22 0.3× 62 1.6× 10 585
Lucía Ramos-Alonso Spain 11 280 0.8× 19 0.1× 9 0.1× 24 0.3× 28 0.7× 17 554
Elsie M. Williams New Zealand 10 270 0.7× 26 0.1× 18 0.2× 12 0.1× 30 0.8× 18 466
Atsushi Kameda Japan 12 471 1.3× 29 0.1× 9 0.1× 8 0.1× 45 1.1× 20 627
Tobias Kruse Germany 14 275 0.8× 14 0.1× 34 0.4× 227 2.8× 76 1.9× 30 652
Robert Yan United Kingdom 11 221 0.6× 13 0.1× 23 0.3× 161 2.0× 23 0.6× 18 395

Countries citing papers authored by Chengpin Shen

Since Specialization
Citations

This map shows the geographic impact of Chengpin Shen'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 Chengpin Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chengpin Shen more than expected).

Fields of papers citing papers by Chengpin Shen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chengpin Shen. 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 Chengpin Shen. The network helps show where Chengpin Shen may publish in the future.

Co-authorship network of co-authors of Chengpin Shen

This figure shows the co-authorship network connecting the top 25 collaborators of Chengpin Shen. A scholar is included among the top collaborators of Chengpin Shen 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 Chengpin Shen. Chengpin Shen 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.
Yang, Shuang, Jun Yao, Guoquan Yan, et al.. (2024). Multidimensional Proteomic Landscape Reveals Distinct Activated Pathways Between Human Brain Tumors. Advanced Science. 12(7). e2410142–e2410142.
2.
Mallawaarachchi, Vijini, Jinzhi Zhao, Yi Yang, et al.. (2024). Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics. Microbiome. 12(1). 58–58. 5 indexed citations
3.
Yang, Yi, et al.. (2024). Deep Learning Powers Protein Identification from Precursor MS Information. Journal of Proteome Research. 23(9). 3837–3846. 3 indexed citations
4.
Yang, Yi, et al.. (2024). High-Abundance Protein-Guided Hybrid Spectral Library for Data-Independent Acquisition Metaproteomics. Analytical Chemistry. 96(3). 1029–1037. 6 indexed citations
5.
Zong, Yu, Yuxin Wang, Yi Yang, et al.. (2023). DeepFLR facilitates false localization rate control in phosphoproteomics. Nature Communications. 14(1). 2269–2269. 10 indexed citations
6.
Zhao, Jinzhi, Yi Yang, Liangqiang Chen, et al.. (2023). Quantitative metaproteomics reveals composition and metabolism characteristics of microbial communities in Chinese liquor fermentation starters. Frontiers in Microbiology. 13. 1098268–1098268. 13 indexed citations
7.
Zhao, Jinzhi, Yi Yang, Bing Wang, et al.. (2023). Metaproteomics profiling of the microbial communities in fermentation starters (Daqu) during multi-round production of Chinese liquor. Frontiers in Nutrition. 10. 1139836–1139836. 12 indexed citations
8.
Zhao, Jinzhi, Yi Yang, Hua Xu, et al.. (2023). Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota. npj Biofilms and Microbiomes. 9(1). 4–4. 39 indexed citations
9.
Huang, Peiwu, Xiang Liu, Ao Zhang, et al.. (2023). Comprehensive Evaluation and Optimization of the Data-Dependent LC–MS/MS Workflow for Deep Proteome Profiling. Analytical Chemistry. 95(20). 7897–7905. 1 indexed citations
10.
Wang, Xiaoqing, et al.. (2022). Multi-Omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial Cancer. Frontiers in Oncology. 12. 861142–861142. 12 indexed citations
11.
Zhao, Jinzhi, Yuning Wang, Beibei Yang, et al.. (2021). Mesoporous Silica as Sorbents and Enzymatic Nanoreactors for Microbial Membrane Proteomics. ACS Applied Materials & Interfaces. 13(10). 11571–11578. 8 indexed citations
12.
Jia, Yi, Yi Yang, Chengpin Shen, et al.. (2020). Direct MALDI-TOF profiling of gingival crevicular fluid sediments for periodontitis diagnosis. Talanta. 225. 121956–121956. 7 indexed citations
13.
Yang, Yi, Xiaohui Liu, Chengpin Shen, et al.. (2020). In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics. Nature Communications. 11(1). 146–146. 157 indexed citations
14.
Long, Shuping, Yi Yang, Chengpin Shen, et al.. (2020). Metaproteomics characterizes human gut microbiome function in colorectal cancer. npj Biofilms and Microbiomes. 6(1). 14–14. 96 indexed citations
15.
Yu, Yanyan, Xiaohui Liu, Hailin Tang, et al.. (2013). An iTRAQ based quantitative proteomic strategy to explore novel secreted proteins in metastatic hepatocellular carcinoma cell lines. The Analyst. 138(16). 4505–4505. 17 indexed citations
16.
Xie, Liqi, Chengpin Shen, Minbo Liu, et al.. (2012). Improved proteomic analysis pipeline for LC-ETD-MS/MS using charge enhancing methods. Molecular BioSystems. 8(10). 2692–2698. 5 indexed citations
17.
Shen, Chengpin, Yanyan Yu, Hong Li, et al.. (2012). Global profiling of proteolytically modified proteins in human metastatic hepatocellular carcinoma cell lines reveals CAPN2 centered network. PROTEOMICS. 12(12). 1917–1927. 22 indexed citations
18.
Cao, Jing, Chengpin Shen, Jun Zhang, et al.. (2011). Comparison of alternative extraction methods for secretome profiling in human hepatocellular carcinoma cells. Science China Life Sciences. 54(1). 34–38. 9 indexed citations
19.
20.
Cao, Jing, Yuanyuan Hu, Chengpin Shen, et al.. (2009). Nanozeolite‐driven approach for enrichment of secretory proteins in human hepatocellular carcinoma cells. PROTEOMICS. 9(21). 4881–4888. 17 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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