Xin Niu

5.4k citations
87 papers · 4.0k indexed · 2 hit papers · h-index 29
Topics
Extracellular vesicles in disease (20 papers)Advanced Neural Network Applications (13 papers)MicroRNA in disease regulation (9 papers)
Journals
Advanced MaterialsSHILAP Revista de lepidopterologíaPLoS ONE

In The Last Decade

Xin Niu

79 papers receiving 4.0k citations

Hit Papers

Exosomes Secreted by Human-Induced Pluripotent Stem Cell-...201620262019202220162017100200300400

Peers

Xin Niu
Comparison fields: 5 of 156
  • Molecular Biology 2.4k
  • Cancer Research 1.3k
  • Genetics 598
  • Surgery 571
  • Biomedical Engineering 547
Replace Wei Tang with:
Wei Tang China
Qin Han China
Kenji Hata Japan
Hong Bu China
Bin Gu China
Ben D. MacArthur United Kingdom
Ran Xu China
Hung Pham United States
Athanasios Mantalaris United Kingdom
Peter C. Brooks United States
Xin Niu relative to Wei Tang China Wei Tang's profile →
Citations per field
00.5×3.7×
Wei Tang · 1×
Citations per year

Countries citing papers authored by Xin Niu

Since Specialization
Citations

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

Fields of papers citing papers by Xin Niu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xin Niu

This figure shows the co-authorship network connecting the top 25 collaborators of Xin Niu. A scholar is included among the top collaborators of Xin Niu 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 Xin Niu. Xin Niu 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
#WorkIndexed citations
1 1
2 3
3 13
4 0
5 3
6 0
7 67
8 75
9 8
10 44
11 57
12 25
13 208
14 58
15 25
16 137
17 26
18
Clinical trial and assessment of a portable apparatus for auxiliary diagnosis and treatment based on four TCM diagnostic methods
1
19 34
20 73

About Xin Niu

Xin Niu is a scholar working on Computer Vision and Pattern Recognition, Genetics and Process Chemistry and Technology, having authored 87 papers that have together received 4.0k indexed citations. Recurring topics across this work include Extracellular vesicles in disease (20 papers), Advanced Neural Network Applications (13 papers) and MicroRNA in disease regulation (9 papers). The work is most often cited by research in Cancer Research (1.3k citations), Genetics (598 citations) and Urology (231 citations). Xin Niu has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Qing Li, Yang Wang, Bin Hu, Jieyuan Zhang, Yang Wang, Bizeng Zhao, Chun‐Yuan Chen, Haiyan Li, Yunlong Yang and Changqing Zhang. Their work appears in journals such as Advanced Materials, SHILAP Revista de lepidopterología and PLoS ONE.

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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