Si Wu

5.0k total citations · 1 hit paper
145 papers, 3.7k citations indexed

About

Si Wu is a scholar working on Spectroscopy, Molecular Biology and Oncology. According to data from OpenAlex, Si Wu has authored 145 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Spectroscopy, 60 papers in Molecular Biology and 17 papers in Oncology. Recurrent topics in Si Wu's work include Mass Spectrometry Techniques and Applications (60 papers), Advanced Proteomics Techniques and Applications (56 papers) and Metabolomics and Mass Spectrometry Studies (23 papers). Si Wu is often cited by papers focused on Mass Spectrometry Techniques and Applications (60 papers), Advanced Proteomics Techniques and Applications (56 papers) and Metabolomics and Mass Spectrometry Studies (23 papers). Si Wu collaborates with scholars based in United States, China and Netherlands. Si Wu's co-authors include Ljiljana Paša‐Tolić, Richard Smith, Kellye A. Cupp‐Sutton, Xiaowen Liu, Nikola Tolić, David L. Stenoien, Zhaorui Zhang, Ronald Moore, Errol Robinson and Xiao Lin and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Si Wu

129 papers receiving 3.6k citations

Hit Papers

align trajectories

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Si Wu United States	33	1.9k	1.6k	365	363	311	145	3.7k
Raf Van de Plas United States	22	1.2k 0.6×	980 0.6×	178 0.5×	79 0.2×	21 0.1×	48	2.3k
Samuel Payne United States	34	2.7k 1.4×	1.6k 1.0×	240 0.7×	33 0.1×	16 0.1×	108	4.0k
Olga Vitek United States	40	4.2k 2.2×	3.0k 1.9×	269 0.7×	26 0.1×	12 0.0×	99	6.4k
Wu Liu China	35	1.2k 0.6×	981 0.6×	1.1k 2.9×	26 0.1×	40 0.1×	193	4.1k
Ali M. Ardekani Iran	21	2.5k 1.3×	1.3k 0.8×	165 0.5×	75 0.2×	6 0.0×	64	4.0k
Wolfram Gronwald Germany	31	2.0k 1.0×	526 0.3×	200 0.5×	11 0.0×	27 0.1×	101	3.2k
Sushmita Roy United States	40	3.8k 2.0×	620 0.4×	213 0.6×	18 0.0×	13 0.0×	131	5.5k
Yiming Yang China	28	1.1k 0.6×	63 0.0×	270 0.7×	445 1.2×	31 0.1×	112	3.2k
Keisuke Miyazawa Japan	43	2.4k 1.2×	49 0.0×	74 0.2×	146 0.4×	218 0.7×	196	5.6k
Parag Mallick United States	35	4.8k 2.5×	2.8k 1.8×	764 2.1×	20 0.1×	12 0.0×	98	7.3k

Countries citing papers authored by Si Wu

Since Specialization

Citations

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

Fields of papers citing papers by Si Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Si Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Si Wu. A scholar is included among the top collaborators of Si Wu 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 Si Wu. Si Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Wu, Si, Hong Bo Liu, Kun Wang, et al.. (2025). HER2 testing in multifocal/multicentric breast cancer: should all foci be tested in the context of HER2-low and HER2-ultralow?. The Breast. 84. 104572–104572.

2.

Cupp‐Sutton, Kellye A., et al.. (2024). Characterizing age-related changes in intact mitochondrial proteoforms in murine hearts using quantitative top-down proteomics. Clinical Proteomics. 21(1). 57–57. 7 indexed citations

3.

Wu, Si, et al.. (2024). Evaluating seasonal chiller performance using operational data. Applied Energy. 377. 124377–124377. 6 indexed citations

4.

Wu, Si, Jiale Gao, H. Jaap Bonjer, et al.. (2024). Definition and grading of anastomotic leakage following right hemicolectomy. British journal of surgery. 111(7). 2 indexed citations

5.

Yao, Hongwei, Jiale Gao, Zhengyang Yang, et al.. (2024). Efficacy and safety of neoadjuvant chemoradiotherapy combined with tislelizumab and thymalfasin in the treatment of locally advanced lower rectal cancer.. Journal of Clinical Oncology. 42(16_suppl). e15612–e15612.

6.

Wang, Xinran, Chang Liu, Xuemei Sun, et al.. (2023). Effect of storage time of paraffin sections on the expression of PD-L1 (SP142) in invasive breast cancer. Diagnostic Pathology. 18(1). 131–131. 2 indexed citations

7.

Sun, Xuemei, et al.. (2023). Evolution and clinical significance of HER2-low status after neoadjuvant therapy for breast cancer. Frontiers in Oncology. 13. 1086480–1086480. 12 indexed citations

8.

Li, Yifei, Jinzhao Liu, Zihang Xu, et al.. (2023). Construction and validation of a nomogram for predicting the prognosis of patients with lymph node-positive invasive micropapillary carcinoma of the breast: based on SEER database and external validation cohort. Frontiers in Oncology. 13. 1231302–1231302. 3 indexed citations

9.

Guo, Yanting, et al.. (2023). Multidimensional separations in top–down proteomics. SHILAP Revista de lepidopterología. 4(5-6). 181–203. 16 indexed citations

10.

Wu, Si, Yue Meng, Jun Zhang, et al.. (2023). The Role of Artificial Intelligence in Accurate Interpretation of HER2 Immunohistochemical Scores 0 and 1+ in Breast Cancer. Modern Pathology. 36(3). 100054–100054. 63 indexed citations

11.

Cupp‐Sutton, Kellye A. & Si Wu. (2020). High-throughput quantitative top-down proteomics. Molecular Omics. 16(2). 91–99. 83 indexed citations

12.

Wang, Lizhe, et al.. (2020). Tile‐based polygon overlay of vector data in a virtual globe. Transactions in GIS. 24(4). 1033–1046. 1 indexed citations

13.

Schaffer, Leah V., Robert J. Millikin, Rachel Miller, et al.. (2019). Identification and Quantification of Proteoforms by Mass Spectrometry. PMC.

14.

Wu, Si, Yang Zhang, Lian Zhang, et al.. (2018). Methylation and Expression of Nonclustered Protocadherins Encoding Genes and Risk of Precancerous Gastric Lesions in a High-Risk Population. Cancer Prevention Research. 11(11). 717–726. 3 indexed citations

15.

Dekker, Lennard J. M., Si Wu, Martijn M. VanDuijn, et al.. (2017). De Novo Sequencing of Peptides from High‐Resolution Bottom‐Up Tandem Mass Spectra using Top‐Down Intended Methods. PROTEOMICS. 17(23-24). 6 indexed citations

16.

Deng, Fei, et al.. (2017). A Visualization Plan for Large-scale 3D Models with Oblique Photogrammetry Technology. Bulletin of Surveying and Mapping. 39. 4 indexed citations

17.

Li, Jingjia, et al.. (2013). Retrospective review of 190 patients treated for parotid tumors: a single institute experience. Chinese Medical Journal. 126(5). 988–990.

18.

Wu, Si. (2005). A Study on the Content Delivery Performance of www.Ctrip.com,www.China-sss.com,www.elong.com. Luyou xuekan.

19.

Wu, Si. (2003). Array geometry based on fixed elements. Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University. 2 indexed citations

20.

Wu, Si. (2000). Miss Distance Measurement for Targets with Rectilinear Trajectories and Constant Accelerations. Dianzi xuebao. 1 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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