Song Wu

3.3k total citations
137 papers, 2.2k citations indexed

About

Song Wu is a scholar working on Molecular Biology, Cancer Research and Infectious Diseases. According to data from OpenAlex, Song Wu has authored 137 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Molecular Biology, 22 papers in Cancer Research and 18 papers in Infectious Diseases. Recurrent topics in Song Wu's work include Antimicrobial Resistance in Staphylococcus (16 papers), Biochemical and Structural Characterization (15 papers) and Cancer-related molecular mechanisms research (14 papers). Song Wu is often cited by papers focused on Antimicrobial Resistance in Staphylococcus (16 papers), Biochemical and Structural Characterization (15 papers) and Cancer-related molecular mechanisms research (14 papers). Song Wu collaborates with scholars based in China, United States and Hong Kong. Song Wu's co-authors include Shuang Jiang, Jinhuan Wei, Zhenhua Chen, Junhang Luo, Ming Hu, Jiaxing Zhang, Wei Chen, Hui‐Yun Wang, Bing Liao and Yi-Ji Liao and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Song Wu

129 papers receiving 2.2k citations

Peers

Song Wu
Comparison fields: 5 of 132
  • Molecular Biology 1.2k
  • Cancer Research 567
  • Oncology 256
  • Pathology and Forensic Medicine 229
  • Pulmonary and Respiratory Medicine 225
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Citations per field, relative to Song Wu
Song Wu · 1×
Citations per year, relative to Song Wu
Song Wu · 1×

Countries citing papers authored by Song Wu

Since Specialization
Citations

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

Fields of papers citing papers by Song Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Song Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Song Wu. A scholar is included among the top collaborators of Song 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 Song Wu. Song 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 1
3 2
4 4
5 20
6 4
7 4
8 0
9 9
10 7
11 9
12 8
13 11
14 20
15 14
16
MicroRNA-936 targets FGF2 to inhibit epithelial ovarian cancer aggressiveness by deactivating the PI3K/Akt pathway
0
17
TRIM14 promotes cell proliferation and inhibits apoptosis by suppressing PTEN in colorectal cancer
3
18 36
19
Preliminary Study on Literature Search of Neiguan Point
0
20
Improvements on the new relaxation & bubble point method in the continuous distillation calculation
1

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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