Ge Wang

859 citations
19 papers · 576 · h-index 10

Impact in

Papers in

    • Circular RNAs in diseases 3
    • RNA modifications and cancer 3
    • Machine Learning in Bioinformatics 1
    • Cancer-related molecular mechanisms research 5
    • MicroRNA in disease regulation 1

Ge Wang

18 papers receiving 569 citations

Peers

Ge Wang
Comparison fields: 5 of 107
  • Cancer Research 227
  • Communication 39
  • Information Systems and Management 31
  • Molecular Biology 280
  • Cell Biology 48
Replace Jing Lv with:
Jing Lv China
Hua Wei China
Yun Deng China
J. Leon Zhao China
Jiwoon Park United States
Xuan Yu China
Stephanie Jean Tsang Hong Kong
Houcai Wang China
Ge Wang relative to Jing Lv China Jing Lv's profile →
Citations per field
00.5×1.5×2.3×
Jing Lv · 1×
Citations per year

Countries citing papers authored by Ge Wang

Since Specialization
Citations

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

Fields of papers citing papers by Ge Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ge Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ge Wang Line = papers co-authored together Ge Wang links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 2017157
2 2018115
3 200973
4 201660
5 201542
6 201831
7 202127
8 201618
9
Madecassic acid inhibits the mouse colon cancer growth by inducing apoptosis and immunomodulation.
201416
10 202013
11 20217
12 20144
13 20084
14 20204
15 20192
16 20241
17 20211
18 20071
19 20090

About Ge Wang

Ge Wang is a scholar working on Molecular Biology, Cancer Research, Epidemiology, Social Psychology and Cell Biology, having authored 19 papers that have together received 576 indexed citations. Recurring topics across this work include Cancer-related molecular mechanisms research (5 papers), Circular RNAs in diseases (3 papers), RNA modifications and cancer (3 papers), Cancer Research and Treatments (2 papers), Inflammatory Bowel Disease (2 papers), MicroRNA in disease regulation (1 paper), Machine Learning in Bioinformatics (1 paper) and Social Media and Politics (1 paper). The work is most often cited by research in Cancer Research (227 citations), Communication (39 citations), Information Systems and Management (31 citations), Molecular Biology (280 citations) and Cell Biology (48 citations). Ge Wang has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Runxi Zeng, Wei Zhang, Jin‐Shui Zhu, Hui Liu, Yanxia Huang, Rui Zhang, Lidan Hou, Xiaoyu Chen, Jing Zhang and Wei Zhang. Their work appears in journals such as International Journal of Medical Sciences, Analytical Chemistry, European Journal of Gastroenterology & Hepatology, Journal of Gastroenterology and Hepatology and Science of Advanced Materials.

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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