Anjun Chen

2.1k citations
34 papers · 1.7k indexed · 1 hit paper · h-index 13

Impact in

Papers in

Anjun Chen

30 papers receiving 1.7k citations

Hit Papers

The selecting: vascular adhesion molecules 1995 · 766 citations
7661995202620052015250500750

Peers

Anjun Chen
Comparison fields: 5 of 129
  • Immunology and Allergy 526
  • Immunology 496
  • Health Informatics 26
  • Molecular Biology 833
  • Hematology 111
Replace Dong Lu with:
Dong Lu China
Masaki Murata Japan
Elizabeth A. Chlipala United States
Michael J. Flaig Germany
Andreas Schröder Germany
Toshiya Nakamura Japan
Joanne Lannigan United States
Sarah C. Mullaly Canada
Constantin E. Orfanos Germany
James M. Grichnik United States
Anjun Chen relative to Dong Lu China Dong Lu's profile →
Citations per field
00.5×4.1×
Dong Lu · 1×
Citations per year

Countries citing papers authored by Anjun Chen

Since Specialization
Citations

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

Fields of papers citing papers by Anjun Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Anjun Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Anjun Chen Line = papers co-authored together Anjun Chen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20260
2 20250
3 20250
4 20241
5 20240
6 20241
7 20241
8 20243
9 202320
10 20226
11 202210
12 20219
13 201120
14 200829
15 199698
16 1995140
17 199431
18 1994192
19 199337
20 19923

About Anjun Chen

Anjun Chen is a scholar working on Health Informatics, Health Information Management, Immunology and Allergy, Artificial Intelligence and Molecular Biology, having authored 34 papers that have together received 1.7k indexed citations. Recurring topics across this work include Plant biochemistry and biosynthesis (6 papers), Machine Learning in Healthcare (5 papers), Enzyme Structure and Function (4 papers), Microbial Natural Products and Biosynthesis (3 papers), Artificial Intelligence in Healthcare (3 papers), Biomedical Text Mining and Ontologies (3 papers), Artificial Intelligence in Healthcare and Education (3 papers) and Cell Adhesion Molecules Research (3 papers). The work is most often cited by research in Immunology and Allergy (526 citations), Immunology (496 citations), Health Informatics (26 citations), Molecular Biology (833 citations) and Hematology (111 citations). Anjun Chen has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Thomas F. Tedder, Pablo Engel, Douglas A. Steeber, C. Dale Poulter, Ping‐Chiang Lyu, Neville R. Kallenbach, Kevin L. Moore, Martha Delahunty, Susan R. Watson and R P McEver. Their work appears in journals such as Journal of Biological Chemistry, Scientific Reports, JAMA Network Open, The Journal of Experimental Medicine and Archives of Biochemistry and Biophysics.

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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