Weida Gong

1.5k total citations
50 papers, 1.1k citations indexed

About

Weida Gong is a scholar working on Molecular Biology, Cancer Research and Surgery. According to data from OpenAlex, Weida Gong has authored 50 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 25 papers in Cancer Research and 12 papers in Surgery. Recurrent topics in Weida Gong's work include Cancer-related molecular mechanisms research (17 papers), RNA modifications and cancer (13 papers) and MicroRNA in disease regulation (12 papers). Weida Gong is often cited by papers focused on Cancer-related molecular mechanisms research (17 papers), RNA modifications and cancer (13 papers) and MicroRNA in disease regulation (12 papers). Weida Gong collaborates with scholars based in China, South Korea and United States. Weida Gong's co-authors include Meilin Wang, Zhengdong Zhang, Haiyan Chu, Qinghong Zhao, Mulong Du, Haixia Zhu, Jianwei Zhou, Gaoxiang Ma, Zhengdong Zhang and Guoquan Tao and has published in prestigious journals such as PLoS ONE, Cancer and Scientific Reports.

In The Last Decade

Weida Gong

50 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Weida Gong China 19 918 689 146 126 120 50 1.1k
Jiandong Wang China 16 635 0.7× 687 1.0× 121 0.8× 140 1.1× 81 0.7× 37 989
Anbang He China 24 1.3k 1.4× 1.2k 1.7× 134 0.9× 209 1.7× 186 1.6× 54 1.6k
Nianyuan Huang United States 14 1.2k 1.3× 979 1.4× 76 0.5× 171 1.4× 170 1.4× 18 1.6k
Barbara Kinga Barták Hungary 18 589 0.6× 522 0.8× 239 1.6× 54 0.4× 125 1.0× 44 890
Feng Qiu China 12 754 0.8× 786 1.1× 69 0.5× 74 0.6× 88 0.7× 17 938
Taichiro Yoshimoto Japan 17 574 0.6× 371 0.5× 165 1.1× 100 0.8× 258 2.1× 37 889
Pawadee Lohavanichbutr United States 14 555 0.6× 356 0.5× 202 1.4× 123 1.0× 146 1.2× 15 925
Hongchun Wu China 18 685 0.7× 465 0.7× 135 0.9× 107 0.8× 37 0.3× 30 919
Keiichi Koshizuka Japan 24 993 1.1× 920 1.3× 129 0.9× 61 0.5× 106 0.9× 37 1.2k
Fang Sun China 12 764 0.8× 729 1.1× 89 0.6× 67 0.5× 52 0.4× 29 1.1k

Countries citing papers authored by Weida Gong

Since Specialization
Citations

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

Fields of papers citing papers by Weida Gong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Weida Gong

This figure shows the co-authorship network connecting the top 25 collaborators of Weida Gong. A scholar is included among the top collaborators of Weida Gong 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 Weida Gong. Weida Gong 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.
Zhang, Yujuan, Kai Lǚ, Hanting Liu, et al.. (2022). Genetic variants in the Hedgehog signaling pathway genes are associated with gastric cancer risk in a Chinese Han population. Journal of Biomedical Research. 36(1). 22–22. 2 indexed citations
2.
Yuan, Chao, Zhenhong Su, Shengjie Liao, et al.. (2021). miR-198 inhibits the progression of renal cell carcinoma by targeting BIRC5. Cancer Cell International. 21(1). 390–390. 8 indexed citations
3.
Wang, Xiaowei, Hanting Liu, Yanling Wu, et al.. (2021). Genetic variants in m6A regulators are associated with gastric cancer risk. Archives of Toxicology. 95(3). 1081–1088. 21 indexed citations
4.
Gao, Jinliang, et al.. (2020). A One-Pot CRISPR/Cas9-Typing PCR for DNA Detection and Genotyping. Journal of Molecular Diagnostics. 23(1). 46–60. 23 indexed citations
5.
Ma, Gaoxiang, Hanting Liu, Mulong Du, et al.. (2019). A genetic variation in the CpG island of pseudogene GBAP1 promoter is associated with gastric cancer susceptibility. Cancer. 125(14). 2465–2473. 23 indexed citations
6.
Zhang, Honghong, Weiyong Zhao, Dongying Gu, et al.. (2018). Association of Antioxidative Enzymes Polymorphisms with Efficacy of Platin and Fluorouracil-Based Adjuvant Therapy in Gastric Cancer. Cellular Physiology and Biochemistry. 48(6). 2247–2257. 14 indexed citations
7.
Gu, Dongying, Rui Zheng, Junyi Xin, et al.. (2018). Evaluation of GWAS-Identified Genetic Variants for Gastric Cancer Survival. EBioMedicine. 33. 82–87. 9 indexed citations
8.
Liu, Hanting, Haixia Zhu, Weihong Shi, et al.. (2018). Genetic variants in XDH are associated with prognosis for gastric cancer in a Chinese population. Gene. 663. 196–202. 5 indexed citations
9.
Zhao, Tingting, Zhi Xu, Dongying Gu, et al.. (2016). The effects of genomic polymorphisms in one-carbon metabolism pathways on survival of gastric cancer patients received fluorouracil-based adjuvant therapy. Scientific Reports. 6(1). 28019–28019. 12 indexed citations
10.
11.
Song, Peng, Weizhi Wang, Guoquan Tao, et al.. (2014). A miR-29c binding site genetic variant in the 3′-untranslated region of LAMTOR3 gene is associated with gastric cancer risk. Biomedicine & Pharmacotherapy. 69. 70–75. 9 indexed citations
12.
Zhang, Gang, Dongying Gu, Qinghong Zhao, et al.. (2014). Genetic variation in C12orf51 is associated with prognosis of intestinal-type gastric cancer in a Chinese population. Biomedicine & Pharmacotherapy. 69. 133–138. 7 indexed citations
13.
Xu, Ming, Guoquan Tao, Meiyun Kang, et al.. (2013). A Polymorphism (rs2295080) in mTOR Promoter Region and Its Association with Gastric Cancer in a Chinese Population. PLoS ONE. 8(3). e60080–e60080. 31 indexed citations
14.
Zhang, Yangmei, Haixia Zhu, Xunlei Zhang, et al.. (2013). Clinical Significance of MYT1L Gene Polymorphisms in Chinese Patients with Gastric Cancer. PLoS ONE. 8(8). e71979–e71979. 8 indexed citations
15.
Wang, Shizhi, Lulu Chen, Qinghong Zhao, et al.. (2013). Effect of TP53 codon 72 and MDM2 SNP309 polymorphisms on survival of gastric cancer among patients who receiving 5-fluorouracil-based postoperative adjuvant chemotherapy. Cancer Chemotherapy and Pharmacology. 71(4). 1073–1082. 9 indexed citations
16.
Zhu, Haixia, Meilin Wang, Bo Lü, et al.. (2012). A Functional Polymorphism in Pre-miR-146a Is Associated with Susceptibility to Gastric Cancer in a Chinese Population. DNA and Cell Biology. 31(7). 1290–1295. 57 indexed citations
17.
Wang, Shui, Yuanyuan Tian, Dongze Wu, et al.. (2012). Genetic variation of CTNNB1 gene is associated with susceptibility and prognosis of gastric cancer in a Chinese population. Mutagenesis. 27(6). 623–630. 30 indexed citations
18.
Xu, Zhi, Haixia Zhu, John M. Luk, et al.. (2012). Clinical significance of SOD2 and GSTP1 gene polymorphisms in Chinese patients with gastric cancer. Cancer. 118(22). 5489–5496. 43 indexed citations
19.
Zhao, Qinghong, Wei Wang, Zhengdong Zhang, et al.. (2011). A genetic variation in APE1 is associated with gastric cancer survival in a Chinese population. Cancer Science. 102(7). 1293–1297. 12 indexed citations
20.
Gong, Weida, et al.. (2011). Polymorphisms of mismatch repair gene hMLH1 and hMSH2 and risk of gastric cancer in a Chinese population. Oncology Letters. 3(3). 591–598. 15 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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