Shu Zheng

18.4k total citations · 2 hit papers
370 papers, 11.2k citations indexed

About

Shu Zheng is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Shu Zheng has authored 370 papers receiving a total of 11.2k indexed citations (citations by other indexed papers that have themselves been cited), including 180 papers in Molecular Biology, 111 papers in Oncology and 82 papers in Cancer Research. Recurrent topics in Shu Zheng's work include Genetic factors in colorectal cancer (40 papers), Colorectal Cancer Screening and Detection (34 papers) and RNA modifications and cancer (30 papers). Shu Zheng is often cited by papers focused on Genetic factors in colorectal cancer (40 papers), Colorectal Cancer Screening and Detection (34 papers) and RNA modifications and cancer (30 papers). Shu Zheng collaborates with scholars based in China, United States and United Kingdom. Shu Zheng's co-authors include Jiekai Yu, Suzhan Zhang, Xuemin Wang, Liangliang Fan, Yiding Chen, Jiaojiao Zhou, Yingkuan Shao, Ting Chen, Kailun Xu and Biting Zhou and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Shu Zheng

361 papers receiving 11.0k citations

Hit Papers

Circulating tumor cells: biology and clinical significance 2020 2026 2022 2024 2021 2020 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shu Zheng China 53 6.1k 3.0k 3.0k 1.4k 1.3k 370 11.2k
Patrick Tan Singapore 62 6.4k 1.1× 3.6k 1.2× 2.7k 0.9× 1.8k 1.3× 988 0.8× 232 12.3k
Massimo Libra Italy 68 8.5k 1.4× 3.2k 1.0× 4.4k 1.5× 1.8k 1.3× 1.8k 1.4× 290 15.9k
Jiří Zavadil United States 56 8.1k 1.3× 2.7k 0.9× 2.9k 1.0× 1.1k 0.8× 1.3k 1.0× 147 12.6k
Yu‐Sun Chang Taiwan 53 4.8k 0.8× 2.0k 0.7× 2.9k 1.0× 654 0.5× 1.2k 0.9× 247 9.0k
Ning Zhang China 62 7.9k 1.3× 5.0k 1.7× 2.5k 0.8× 1.4k 1.0× 1.0k 0.8× 564 14.5k
Daniel E. Johnson United States 46 8.4k 1.4× 2.3k 0.7× 4.3k 1.5× 1.5k 1.0× 2.2k 1.7× 133 13.6k
Vassilis G. Gorgoulis Greece 63 11.6k 1.9× 3.2k 1.1× 5.4k 1.8× 1.5k 1.0× 2.0k 1.5× 331 18.5k
Douglas M. Noonan Italy 66 6.0k 1.0× 2.1k 0.7× 3.3k 1.1× 741 0.5× 3.0k 2.3× 245 13.7k
Hong Zhao China 59 4.7k 0.8× 1.7k 0.6× 3.9k 1.3× 1.4k 1.0× 2.9k 2.2× 273 12.5k
Alberto M. Martelli Italy 71 12.7k 2.1× 2.1k 0.7× 3.4k 1.2× 1.0k 0.7× 1.7k 1.3× 417 19.2k

Countries citing papers authored by Shu Zheng

Since Specialization
Citations

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

Fields of papers citing papers by Shu Zheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shu Zheng

This figure shows the co-authorship network connecting the top 25 collaborators of Shu Zheng. A scholar is included among the top collaborators of Shu Zheng 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 Shu Zheng. Shu Zheng 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.
Du, Liping, et al.. (2024). Development and application of an intelligent pressure injury assessment system using AI image recognition. Technology and Health Care. 33(3). 1358–1366. 1 indexed citations
2.
Sun, Yiwei, Wei Zhang, Zhiwen Luo, et al.. (2024). ZnO‐CuS/F127 Hydrogels with Multienzyme Properties for Implant‐Related Infection Therapy by Inhibiting Bacterial Arginine Biosynthesis and Promoting Tissue Repair. Advanced Functional Materials. 35(8). 50 indexed citations
3.
Su, Zhiwei, Wangxiong Hu, Xianggui Yuan, et al.. (2023). miR-433 Inhibits Glioblastoma Progression by Suppressing the PI3K/Akt Signaling Pathway Through Direct Targeting of ERBB4. OMICS A Journal of Integrative Biology. 27(5). 215–226. 4 indexed citations
4.
Xu, Kailun, Shu Zheng, Baosheng Li, Yingkuan Shao, & Xiaoyang Yin. (2023). Molecular characterization of colorectal mucinous adenocarcinoma and adenocarcinoma, not otherwise specified, identified by multiomic data analysis. Frontiers in Molecular Biosciences. 10. 1150362–1150362. 2 indexed citations
5.
Zhu, Yingshuang, Yanqin Huang, Yeting Hu, et al.. (2021). Long‐term risk of colorectal cancer after removal of adenomas during screening colonoscopies in a large community‐based population in China. International Journal of Cancer. 150(4). 594–602. 6 indexed citations
8.
Hu, Wangxiong, Yanmei Yang, Xiaofen Li, et al.. (2018). Multi-omics Approach Reveals Distinct Differences in Left- and Right-Sided Colon Cancer. Molecular Cancer Research. 16(3). 476–485. 40 indexed citations
9.
Yuan, Ruoshi, Suzhan Zhang, Jiekai Yu, et al.. (2017). Beyond cancer genes: colorectal cancer as robust intrinsic states formed by molecular interactions. Open Biology. 7(11). 10 indexed citations
10.
Xiao, Qian, Donger Zhou, Agnieszka A. Rucki, et al.. (2016). Cancer-Associated Fibroblasts in Pancreatic Cancer Are Reprogrammed by Tumor-Induced Alterations in Genomic DNA Methylation. Cancer Research. 76(18). 5395–5404. 92 indexed citations
11.
Liang, Qiaoyi, Jonathan Chiu, Yingxuan Chen, et al.. (2016). Fecal Bacteria Act as Novel Biomarkers for Noninvasive Diagnosis of Colorectal Cancer. Clinical Cancer Research. 23(8). 2061–2070. 248 indexed citations
12.
Sung, Joseph J.�Y., Siew C. Ng, Francis K.L. Chan, et al.. (2014). An updated Asia Pacific Consensus Recommendations on colorectal cancer screening. Gut. 64(1). 121–132. 302 indexed citations
14.
Hu, Hanguang, Hang Zhang, Weiting Ge, et al.. (2012). Secreted Protein Acidic and Rich in Cysteines-like 1 Suppresses Aggressiveness and Predicts Better Survival in Colorectal Cancers. Clinical Cancer Research. 18(19). 5438–5448. 38 indexed citations
15.
Zhang, Suzhan, Honghong Zhu, Yanqin Huang, et al.. (2011). Performance of a Colorectal Cancer Screening Protocol in an Economically and Medically Underserved Population. Cancer Prevention Research. 4(10). 1572–1579. 28 indexed citations
16.
Liu, Xiyong, Lily Lai, Xiaochen Wang, et al.. (2011). Ribonucleotide Reductase Small Subunit M2B Prognoses Better Survival in Colorectal Cancer. Cancer Research. 71(9). 3202–3213. 52 indexed citations
17.
Wang, Xiaochen, Xiyong Liu, Lirong Chen, et al.. (2011). Overexpression of HMGA2 Promotes Metastasis and Impacts Survival of Colorectal Cancers. Clinical Cancer Research. 17(8). 2570–2580. 151 indexed citations
18.
Zheng, Shu. (2009). Effects of RNA interference polo-like kinase-1 gene on telomerase activity of human colon cancer cell. 1 indexed citations
19.
Meng, Wen, et al.. (2009). Performance value of high risk factors in colorectal cancerscreening in China. World Journal of Gastroenterology. 15(48). 6111–6111. 42 indexed citations
20.
Zheng, Shu. (2004). Effect of Elemene on the Telomerase Activity ,Apoptosis and Cell Cycles of the Colon Cancer Lovo Cell Line. Yiyao daobao. 3 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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