Sue Chan

21 papers receiving 553 citations

Peers

Sue Chan
Comparison fields: 5 of 102
  • Biochemistry 55
  • Physiology 32
  • Pharmacology 103
  • Cellular and Molecular Neuroscience 116
  • Endocrine and Autonomic Systems 27
Replace Mitsumasa Mankura with:
Mitsumasa Mankura Japan
Elisa Vivoli Italy
Mohaddeseh Sadat Alavi Iran
Stella Calafato United States
Chiara Lanzillotta Italy
Anne‐Dominique Lajoix France
A.P.M. Yusof Malaysia
F.K. Okwuasaba Nigeria
André Simões Pires Brazil
Shinichi Uchida Japan
Sue Chan relative to Mitsumasa Mankura Japan Mitsumasa Mankura's profile →
Citations per field
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Mitsumasa Mankura · 1×
Citations per year

Countries citing papers authored by Sue Chan

Since Specialization
Citations

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

Fields of papers citing papers by Sue Chan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017160
2 201761
3 200156
4 199954
5 200133
6 198930
7 199629
8 199927
9 201324
10 201417
11 200216
12 199915
13 201910
14 20018
15 20138
16 20118
17 20067
18 20135
19 20042
20 20201

About Sue Chan

Sue Chan is a scholar working on Molecular Biology, Surgery, Cellular and Molecular Neuroscience, Pharmacology and Endocrinology, Diabetes and Metabolism, having authored 22 papers that have together received 572 indexed citations. Recurring topics across this work include Pancreatic function and diabetes (7 papers), Ion channel regulation and function (6 papers), Receptor Mechanisms and Signaling (4 papers), Neuroscience and Neuropharmacology Research (3 papers), Cannabis and Cannabinoid Research (3 papers), Neurotransmitter Receptor Influence on Behavior (3 papers), Phytochemicals and Antioxidant Activities (2 papers) and Adenosine and Purinergic Signaling (2 papers). The work is most often cited by research in Biochemistry (55 citations), Physiology (32 citations), Pharmacology (103 citations), Cellular and Molecular Neuroscience (116 citations) and Endocrine and Autonomic Systems (27 citations). Sue Chan has collaborated with scholars based in United Kingdom, United States and Sweden. Frequent co-authors include Noel G. Morgan, Margaret K. Pratten, Jeffrey R. Fry, Christopher A. Ramsden, S P H Alexander, Jenni Harvey, Andrew J. Irving, Klaus Turnheim, James Costantin and Stanley G. Schultz. Their work appears in journals such as British Journal of Pharmacology, Journal of Endocrinology, Biochemical and Biophysical Research Communications, Journal of Pharmacology and Experimental Therapeutics and American Journal of Physiology-Endocrinology and Metabolism.

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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