Sheng Chen

61 papers receiving 1.1k citations

Peers

Sheng Chen
Comparison fields: 5 of 127
  • Aging 42
  • Endocrine and Autonomic Systems 78
  • Cellular and Molecular Neuroscience 187
  • Molecular Biology 480
  • Physiology 157
Replace Humberto Arboleda with:
Humberto Arboleda Colombia
Caroline Dalton United Kingdom
Aaron C. Pawlyk United States
Katherine Beebe United States
Ana Dı́ez-Sampedro United States
Shiping Li China
Elena Cellini Italy
Bunpei Ishizuka Japan
Sivan Vadakkadath Meethal United States
Suk Ling Hong Kong
Sheng Chen relative to Humberto Arboleda Colombia Humberto Arboleda's profile →
Citations per field
00.5×1.5×2.0×
Humberto Arboleda · 1×
Citations per year

Countries citing papers authored by Sheng Chen

Since Specialization
Citations

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

Fields of papers citing papers by Sheng Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Sheng 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 Sheng Chen Line = papers co-authored together Sheng Chen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1990101
2 201085
3 201482
4 200778
5 200577
6 200251
7 200447
8 201544
9 200743
10 201239
11 200738
12 201234
13 201033
14 200129
15 201626
16 201124
17 202022
18 200418
19 201718
20 200418

About Sheng Chen

Sheng Chen is a scholar working on Molecular Biology, Oncology, Applied Psychology, Cellular and Molecular Neuroscience and Public Health, Environmental and Occupational Health, having authored 66 papers that have together received 1.1k indexed citations. Recurring topics across this work include Heat shock proteins research (8 papers), Digital Mental Health Interventions (8 papers), Neuroscience and Neuropharmacology Research (3 papers), Cannabis and Cannabinoid Research (3 papers), RNA Interference and Gene Delivery (2 papers), Receptor Mechanisms and Signaling (2 papers), HER2/EGFR in Cancer Research (2 papers) and Biochemical effects in animals (2 papers). The work is most often cited by research in Aging (42 citations), Endocrine and Autonomic Systems (78 citations), Cellular and Molecular Neuroscience (187 citations), Molecular Biology (480 citations) and Physiology (157 citations). Sheng Chen has collaborated with scholars based in Canada, China and United States. Frequent co-authors include Ian R. Brown, James W. Gurd, Shintaro Besshoh, Chengfeng Xiao, Tangchun Wu, Robert M. Tanguay, Fang Liu, Ruibo Wang, Philip Seeman and Michele J. Grimm. Their work appears in journals such as Cell Stress and Chaperones, PLoS ONE, The Canadian Journal of Psychiatry, Frontiers in Immunology and Brain Research.

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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