Gang Dong

804 citations
14 papers · 682 indexed · h-index 10
Topics
Cancer-related molecular mechanisms research (6 papers)MicroRNA in disease regulation (5 papers)NF-κB Signaling Pathways (5 papers)
Partner nations
ChinaUnited StatesJapan

In The Last Decade

Gang Dong

13 papers receiving 669 citations

Peers

Gang Dong
Comparison fields: 5 of 74
  • Molecular Biology 450
  • Cancer Research 435
  • Oncology 223
  • Immunology 136
  • Pulmonary and Respiratory Medicine 38
Replace Chia Wen Tsai with:
Chia Wen Tsai Taiwan
Lalita Yadav India
Robert Vander Broek United States
Ya‐Ching Lu Taiwan
Chang‐Chieh Wu Taiwan
Kwang Hwa Park South Korea
Junfang Ji China
Souhir Mestiri Tunisia
Domenico Zito Italy
Suryanaryana V.S. Deo India
Gang Dong relative to Chia Wen Tsai Taiwan Chia Wen Tsai's profile →
Citations per field
00.5×
Chia Wen Tsai · 1×
Citations per year

Countries citing papers authored by Gang Dong

Since Specialization
Citations

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

Fields of papers citing papers by Gang Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gang Dong

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

All Works

14 of 14 papers shown
#WorkIndexed citations
1 0
2 9
3 14
4 27
5 22
6 1
7 6
8
Association between microRNA polymorphisms and papillary thyroid cancer susceptibility.
16
9 66
10 4
11 56
12 192
13
Expression of a dominant-negative mutant inhibitor-kappaBalpha of nuclear factor-kappaB in human head and neck squamous cell carcinoma inhibits survival, proinflammatory cytokine expression, and tumor growth in vivo.
231
14 38

About Gang Dong

Gang Dong is a scholar working on Cancer Research, Immunology and Allergy and Molecular Biology, having authored 14 papers that have together received 682 indexed citations. Recurring topics across this work include Cancer-related molecular mechanisms research (6 papers), MicroRNA in disease regulation (5 papers) and NF-κB Signaling Pathways (5 papers). The work is most often cited by research in Cancer Research (435 citations), Oncology (223 citations) and Molecular Biology (450 citations). Gang Dong has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Carter Van Waes, Frank G. Ondrey, Zhong Chen, Dianne C. Duffey, Keith Brown, Ulrich Siebenlist, Naofumi Mukaida, John B. Sunwoo, Jeffrey S. Wolf and Mary Beth DeYoung. Their work appears in journals such as Journal of Biological Chemistry, Clinical Cancer Research and British Journal of Cancer.

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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