Xing Tang

3.2k citations
63 papers · 1.4k indexed · h-index 21
Topics
Pneumocystis jirovecii pneumonia detection and treatment (9 papers)Cancer-related molecular mechanisms research (7 papers)Tuberculosis Research and Epidemiology (7 papers)

In The Last Decade

Xing Tang

58 papers receiving 1.4k citations

Peers

Xing Tang
Comparison fields: 5 of 122
  • Molecular Biology 592
  • Epidemiology 337
  • Infectious Diseases 246
  • Genetics 182
  • Plant Science 161
Replace Tomoko Kobayashi with:
Tomoko Kobayashi Japan
Mansoor Salehi Iran
Masakazu Kamata United States
Jason Dang United States
Claudia Matteucci Italy
Dan Xu China
Gianluigi Lichinchi United States
В. Л. Карпов Russia
Mohammad A. Rafi United States
Rodrigo Madeiro da Costa Brazil
Xing Tang relative to Tomoko Kobayashi Japan Tomoko Kobayashi's profile →
Citations per field
00.5×
Tomoko Kobayashi · 1×
Citations per year

Countries citing papers authored by Xing Tang

Since Specialization
Citations

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

Fields of papers citing papers by Xing Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xing Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Xing Tang. A scholar is included among the top collaborators of Xing Tang 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 Xing Tang. Xing Tang 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
#WorkIndexed citations
1 0
2 0
3 0
4 0
5 13
6 13
7 1
8 5
9 26
10 1
11 17
12 65
13 33
14 41
15 5
16 55
17
[Connective tissue growth factor mediates high glucose-induced down-regulation of podocalyxin expression in mouse podocytes].
2
18 143
19 37
20 30

About Xing Tang

Xing Tang is a scholar working on Health Informatics, Cancer Research and Developmental Neuroscience, having authored 63 papers that have together received 1.4k indexed citations. Recurring topics across this work include Pneumocystis jirovecii pneumonia detection and treatment (9 papers), Cancer-related molecular mechanisms research (7 papers) and Tuberculosis Research and Epidemiology (7 papers). The work is most often cited by research in Infectious Diseases (246 citations), Epidemiology (337 citations) and Cancer Research (140 citations). Xing Tang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Peter L. Goering, Jiwen Zheng, Anurag Mishra, Marilyn S. Bartlett, Ge Gao, August Yue Huang, Liping Wei, Min Zhao, James W. Smith and Hong Yin. Their work appears in journals such as Nucleic Acids Research, Nature Communications and Journal of Clinical Oncology.

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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