Nina L. Siow

588 citations
23 papers · 512 indexed · h-index 14

Nina L. Siow

23 papers receiving 501 citations

Peers

Nina L. Siow
Comparison fields: 5 of 72
  • Physiology 134
  • Pharmacology 238
  • Cellular and Molecular Neuroscience 152
  • Complementary and alternative medicine 44
  • Computational Theory and Mathematics 77
Replace Tetsade Piermartiri with:
Tetsade Piermartiri Brazil
Ruzhu Chen China
Natàlia Crespo‐Biel Spain
Christine Lange‐Dohna Germany
Rocı́o Salceda Mexico
Preeti J. Khandelwal United States
Mary Kelliher Ireland
M. G. R. PITTA United States
Luz Cortes-Burgos United States
Nina L. Siow relative to Tetsade Piermartiri Brazil Tetsade Piermartiri's profile →
Citations per field
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Citations per year

Countries citing papers authored by Nina L. Siow

Since Specialization
Citations

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

Fields of papers citing papers by Nina L. Siow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nina L. Siow. 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 Nina L. Siow. The network helps show where Nina L. Siow may publish in the future.

Co-authorship network

The 23 scholars most cited alongside Nina L. Siow, 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 Nina L. Siow Line = papers co-authored together Nina L. Siow links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201312
2 201015
3 20089
4 200715
5 20071
6 20067
7 20061
8 200513
9 200510
10 20053
11 200517
12 200434
13 20049
14 200444
15 200341
16 200313
17 200318
18 200129
19 200014
20 200026

About Nina L. Siow

Nina L. Siow is a scholar working on Physiology, Pharmacology, Cellular and Molecular Neuroscience, Complementary and alternative medicine and Computational Theory and Mathematics, having authored 23 papers that have together received 512 indexed citations. Recurring topics across this work include Cholinesterase and Neurodegenerative Diseases (15 papers), Adenosine and Purinergic Signaling (7 papers), Ion channel regulation and function (7 papers), Neuroscience and Neuropharmacology Research (6 papers), Computational Drug Discovery Methods (5 papers), Phosphodiesterase function and regulation (4 papers), Medicinal Plants and Neuroprotection (3 papers) and Pesticide Exposure and Toxicity (3 papers). The work is most often cited by research in Physiology (134 citations), Pharmacology (238 citations), Cellular and Molecular Neuroscience (152 citations), Complementary and alternative medicine (44 citations) and Computational Theory and Mathematics (77 citations). Nina L. Siow has collaborated with scholars based in Hong Kong, United Kingdom and France. Frequent co-authors include Karl Wah Keung Tsim, Roy C.Y. Choi, Joy X. Jiang, David Chi‐Cheong Wan, Karen Ling, Joseph Simon, Eric A. Barnard, Heidi Q. Xie, Henry H.C. Lee and A.K.L. Ting. Their work appears in journals such as Journal of Biological Chemistry, Chemico-Biological Interactions, Molecular Pharmacology, Neuroreport and Journal of Neurochemistry.

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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