Feng Pan

3.3k citations
56 papers · 2.0k · 1 hit paper · h-index 15

Impact in

Papers in

Feng Pan

50 papers receiving 1.9k citations

Feng Pan's Hit Papers

AmberTools 2023 · 1.2k citations
1.2k0+1+2Years since publication2505007501000

Peers

Feng Pan
Comparison fields: 5 of 155
  • Hardware and Architecture 202
  • Computer Networks and Communications 324
  • Molecular Biology 841
  • Computational Theory and Mathematics 155
  • Cellular and Molecular Neuroscience 144
Replace Zhijun Wang with:
Zhijun Wang China
Zhenying Liu China
Ali Reza Shafiee United States
Miran Kim South Korea
Kenji Ohmori Japan
Yanjie Wei China
Garima Thakur India
Wei‐Chung Cheng Taiwan
Hidde de Jong France
Weixun Wang United States
Feng Pan relative to Zhijun Wang China Zhijun Wang's profile →
Citations per field
00.5×2×3.3×
Zhijun Wang · 1×
Citations per year

Countries citing papers authored by Feng Pan

Since Specialization
Citations

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

Fields of papers citing papers by Feng Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1
AmberTools
Hit paper breakdown →
20231163
2 2007142
3 2005101
4 201683
5 202456
6 201453
7 202046
8 201640
9 202026
10 201721
11 201718
12 201918
13 202117
14 202316
15 201815
16 202214
17 202513
18 202512
19 202310
20 202410

About Feng Pan

Feng Pan is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology, Control and Systems Engineering, Computer Networks and Communications and Electrical and Electronic Engineering, having authored 56 papers that have together received 2.0k indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (8 papers), RNA Research and Splicing (7 papers), Genetic Neurodegenerative Diseases (7 papers), Smart Grid Energy Management (5 papers), DNA and Nucleic Acid Chemistry (4 papers), Energy Efficient Wireless Sensor Networks (3 papers), Mobile Ad Hoc Networks (3 papers) and Lipid Membrane Structure and Behavior (2 papers). The work is most often cited by research in Hardware and Architecture (202 citations), Computer Networks and Communications (324 citations), Molecular Biology (841 citations), Computational Theory and Mathematics (155 citations) and Cellular and Molecular Neuroscience (144 citations). Feng Pan has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Christopher Roland, Celeste Sagui, Vincent W. Freeh, David K. Lowenthal, Barry Rountree, Viet Hoang Man, Keith Weninger, Yuan Zhang, Meijuan Gao and Kaoru Ota. Their work appears in journals such as Nucleic Acids Research, The Journal of Physical Chemistry B, Journal of the Science of Food and Agriculture, Biophysical Journal and Nature Machine Intelligence.

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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