Frank W. Pun

2.0k citations
38 papers · 817 indexed · 2 hit papers · h-index 17
Topics
Computational Drug Discovery Methods (7 papers)Epigenetics and DNA Methylation (5 papers)GABA and Rice Research (5 papers)

In The Last Decade

Frank W. Pun

34 papers receiving 793 citations

Hit Papers

AI-powered therapeutic target discovery20232026202420252023202450100150

Peers

Frank W. Pun
Comparison fields: 5 of 112
  • Molecular Biology 427
  • Genetics 178
  • Computational Theory and Mathematics 143
  • Cellular and Molecular Neuroscience 93
  • Cancer Research 53
Replace Izumi V. Hinkson with:
Izumi V. Hinkson United States
Brian M. Schilder United States
Caroline Johnston United Kingdom
Yoko Tajima Japan
Soichi Ogishima Japan
Douglas Arneson United States
Solveig K. Sieberts United States
Hiroshi Tanahashi Japan
Tiziana Alberio Italy
Bruno César Feltes Brazil
Frank W. Pun relative to Izumi V. Hinkson United States Izumi V. Hinkson's profile →
Citations per field
00.5×4.9×
Izumi V. Hinkson · 1×
Citations per year

Countries citing papers authored by Frank W. Pun

Since Specialization
Citations

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

Fields of papers citing papers by Frank W. Pun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Frank W. Pun. 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 Frank W. Pun. The network helps show where Frank W. Pun may publish in the future.

Co-authorship network of co-authors of Frank W. Pun

This figure shows the co-authorship network connecting the top 25 collaborators of Frank W. Pun. A scholar is included among the top collaborators of Frank W. Pun 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 Frank W. Pun. Frank W. Pun 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 0
2 0
3 4
4
PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discoverybreakdown →
65
5 1
6 9
7 15
8 0
9 19
10 11
11 19
12 59
13 5
14 15
15 43
16 25
17 26
18
Two isoforms of GABA(A) receptor beta(2) subunit with different electrophysiological properties: differential expression and genotypical correlations in schizophrenia
16
19 31
20 40

About Frank W. Pun

Frank W. Pun is a scholar working on Aging, Computational Theory and Mathematics and Biophysics, having authored 38 papers that have together received 817 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), Epigenetics and DNA Methylation (5 papers) and GABA and Rice Research (5 papers). The work is most often cited by research in Health Informatics (38 citations), Aging (36 citations) and Biological Psychiatry (28 citations). Frank W. Pun has collaborated with scholars based in Hong Kong, United States and China. Frequent co-authors include Alex Zhavoronkov, Ivan V. Ozerov, Hong Xue, Cunyou Zhao, Jian‐Huan Chen, Zhiliang Yu, Feng Ren, Suk Ying Tsang, Alex Aliper and Xi Long. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

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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