Xuewen Song

3.0k total citations · 1 hit paper
39 papers, 1.9k citations indexed

About

Xuewen Song is a scholar working on Genetics, Molecular Biology and Pathology and Forensic Medicine. According to data from OpenAlex, Xuewen Song has authored 39 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Genetics, 24 papers in Molecular Biology and 8 papers in Pathology and Forensic Medicine. Recurrent topics in Xuewen Song's work include Genetic and Kidney Cyst Diseases (28 papers), Renal and related cancers (18 papers) and Genetic Syndromes and Imprinting (13 papers). Xuewen Song is often cited by papers focused on Genetic and Kidney Cyst Diseases (28 papers), Renal and related cancers (18 papers) and Genetic Syndromes and Imprinting (13 papers). Xuewen Song collaborates with scholars based in Canada, United States and China. Xuewen Song's co-authors include York Pei, Amirreza Haghighi, Matthew B. Lanktree, Feng Qian, Norman D. Rosenblum, Valeria Di Giovanni, Kairong Wang, Ning He, Andrew D. Paterson and Alistair J. Ingram and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Medicine and Nature Materials.

In The Last Decade

Xuewen Song

39 papers receiving 1.9k citations

Hit Papers

Defective glucose metabolism in polycystic kidney disease... 2013 2026 2017 2021 2013 100 200 300

Peers

Xuewen Song
Comparison fields: 5 of 97
  • Molecular Biology 1.3k
  • Genetics 1.3k
  • Pathology and Forensic Medicine 354
  • Nephrology 268
  • Pulmonary and Respiratory Medicine 209
Replace Vishal Patel with:
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Thomas Édouard France
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Vishal Patel United States View profile →
Citations per field, relative to Xuewen Song
Xuewen Song · 1×
Citations per year, relative to Xuewen Song
Xuewen Song · 1×

Countries citing papers authored by Xuewen Song

Since Specialization
Citations

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

Fields of papers citing papers by Xuewen Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xuewen Song

This figure shows the co-authorship network connecting the top 25 collaborators of Xuewen Song. A scholar is included among the top collaborators of Xuewen Song 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 Xuewen Song. Xuewen Song 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 1
2 13
3 5
4 1
5 19
6 48
7 26
8 22
9 80
10 181
11 95
12 36
13 218
14 20
15 27
16 53
17
Defective glucose metabolism in polycystic kidney disease identifies a new therapeutic strategy breakdown →
384
18 12
19 195
20 142

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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