Yu‐Ling Wu

1.2k total citations
41 papers, 970 citations indexed

About

Yu‐Ling Wu is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Immunology. According to data from OpenAlex, Yu‐Ling Wu has authored 41 papers receiving a total of 970 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 9 papers in Cellular and Molecular Neuroscience and 7 papers in Immunology. Recurrent topics in Yu‐Ling Wu's work include Genetic Neurodegenerative Diseases (7 papers), Mitochondrial Function and Pathology (6 papers) and Acute Myeloid Leukemia Research (4 papers). Yu‐Ling Wu is often cited by papers focused on Genetic Neurodegenerative Diseases (7 papers), Mitochondrial Function and Pathology (6 papers) and Acute Myeloid Leukemia Research (4 papers). Yu‐Ling Wu collaborates with scholars based in Taiwan, China and United States. Yu‐Ling Wu's co-authors include Linda M. Boxer, Kirsty Moore, David H. Perlmutter, Paul J. Hippenmeyer, Ernesto P. Molmenti, Magdalena Arcinas, Caroline A. Heckman, Stephen M. Kelsey, Adrian C. Newland and Xu‐Rong Jiang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and The Journal of Immunology.

In The Last Decade

Yu‐Ling Wu

36 papers receiving 950 citations

Peers

Yu‐Ling Wu
Comparison fields: 5 of 89
  • Molecular Biology 550
  • Cancer Research 189
  • Oncology 187
  • Cell Biology 131
  • Epidemiology 112
Replace Ju‐Ock Nam with:
Ju‐Ock Nam South Korea
Jiayuh Lin United States
Timothy Hadden United States
Sivaprakasam Balasubramanian United States
Chang‐Yan Li China
Tomomi Miyamoto Japan
Jeong A. Han South Korea
Wenting Yang China
Xiaoyi Jia China
Zhong‐Fei Shen China
Ju‐Ock Nam South Korea View profile →
Citations per field, relative to Yu‐Ling Wu
Yu‐Ling Wu · 1×
Citations per year, relative to Yu‐Ling Wu
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Countries citing papers authored by Yu‐Ling Wu

Since Specialization
Citations

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

Fields of papers citing papers by Yu‐Ling Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu‐Ling Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Yu‐Ling Wu. A scholar is included among the top collaborators of Yu‐Ling 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 Yu‐Ling Wu. Yu‐Ling 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 3
3 8
4 0
5 5
6 2
7 2
8 9
9 10
10 13
11 6
12 13
13 3
14 0
15 20
16 56
17 55
18 180
19 39
20 10

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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