Hailong Wu

5.4k total citations · 3 hit papers
58 papers, 4.3k citations indexed

About

Hailong Wu is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Hailong Wu has authored 58 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 21 papers in Cancer Research and 8 papers in Oncology. Recurrent topics in Hailong Wu's work include MicroRNA in disease regulation (12 papers), Cancer-related molecular mechanisms research (11 papers) and RNA modifications and cancer (10 papers). Hailong Wu is often cited by papers focused on MicroRNA in disease regulation (12 papers), Cancer-related molecular mechanisms research (11 papers) and RNA modifications and cancer (10 papers). Hailong Wu collaborates with scholars based in China, United States and Mexico. Hailong Wu's co-authors include Yin‐Yuan Mo, Min-Liang Si, Fangting Wu, Shoumin Zhu, Shijie Sheng, Daotai Nie, Yin-Yuan Mo, Mohit Sachdeva, Kounosuke Watabe and Sumit Kumar and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Applied and Environmental Microbiology.

In The Last Decade

Hailong Wu

55 papers receiving 4.3k citations

Hit Papers

MicroRNA-21 Targets the Tumor Suppressor Gene Tropomyosin... 2007 2026 2013 2019 2007 2008 2009 250 500 750

Peers

Hailong Wu
Comparison fields: 5 of 127
  • Molecular Biology 3.4k
  • Cancer Research 2.6k
  • Oncology 452
  • Immunology 263
  • Pulmonary and Respiratory Medicine 188
Replace Qian Zhao with:
Qian Zhao China
Qizhan Liu China
Yang Feng China
Jun He China
Xiaoping Pan China
Ľudmila Vodičková Czechia
Liang Ming China
Fan Yao China
Jharna Datta United States
Ping Yang China
Qian Zhao China View profile →
Citations per field, relative to Hailong Wu
Hailong Wu · 1×
Citations per year, relative to Hailong Wu
Hailong Wu · 1×

Countries citing papers authored by Hailong Wu

Since Specialization
Citations

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

Fields of papers citing papers by Hailong Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hailong Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Hailong Wu. A scholar is included among the top collaborators of Hailong Wu 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 Hailong Wu. Hailong Wu 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
# Work Indexed citations
1 0
2 8
3 5
4 6
5 13
6 34
7 46
8 23
9 8
10 14
11 11
12 55
13 153
14 315
15
p53 represses c-Myc through induction of the tumor suppressor miR-145 breakdown →
691
16 56
17
MicroRNA-21 targets tumor suppressor genes in invasion and metastasis breakdown →
894
18 6
19
MicroRNA-21 Targets the Tumor Suppressor Gene Tropomyosin 1 (TPM1) breakdown →
919
20 21

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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