Zikai Wu
Impact in
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- Computational Drug Discovery Methods
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- Immune Cell Function and Interaction
- T-cell and B-cell Immunology
- Immune Response and Inflammation
Papers in
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- Bioinformatics and Genomic Networks 7
- Microbial Metabolic Engineering and Bioproduction 4
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- Complex Network Analysis Techniques 9
- Opinion Dynamics and Social Influence 5
- Co-authors
- Luonan Chen (7 shared papers)Xing‐Ming Zhao (2 shared papers)Sankar Ghosh (2 shared papers)Will Liao (1 shared paper)Hyun-Ju Oh (1 shared paper)Yenkel Grinberg‐Bleyer (2 shared papers)Nicole Heise (1 shared paper)Pingzhang Wang (2 shared papers)
- Journals
- BMC Systems Biology (2 papers)IET Systems Biology (2 papers)Neurocomputing (1 paper)Planta (1 paper)Molecules and Cells (1 paper)
- Partner nations
- ChinaJapanUnited States
In The Last Decade
Zikai Wu
35 papers receiving 503 citations
Peers
Comparison fields: 5 of 98
- Computational Theory and Mathematics 154
- Immunology 146
- Computational Mathematics 3
- Cancer Research 65
- Molecular Biology 258
Countries citing papers authored by Zikai Wu
This map shows the geographic impact of Zikai Wu'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 Zikai Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zikai Wu more than expected).
Fields of papers citing papers by Zikai Wu
This network shows the impact of papers produced by Zikai Wu. 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 Zikai Wu. The network helps show where Zikai Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Zikai Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 166 | |
| 2 | 2013 | 117 | |
| 3 | 2010 | 68 | |
| 4 | 2009 | 36 | |
| 5 | 2011 | 15 | |
| 6 | 2014 | 14 | |
| 7 | 2024 | 13 | |
| 8 | 2013 | 8 | |
| 9 | 2014 | 8 | |
| 10 | 2012 | 8 | |
| 11 | 2023 | 6 | |
| 12 | 2011 | 6 | |
| 13 | 2017 | 6 | |
| 14 | 2018 | 6 | |
| 15 | 2022 | 5 | |
| 16 | 2013 | 5 | |
| 17 | 2023 | 3 | |
| 18 | 2019 | 3 | |
| 19 | 2019 | 3 | |
| 20 | 2012 | 2 |
About Zikai Wu
Zikai Wu is a scholar working on Molecular Biology, Statistical and Nonlinear Physics, Computational Theory and Mathematics, Plant Science and Condensed Matter Physics, having authored 38 papers that have together received 515 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (9 papers), Computational Drug Discovery Methods (7 papers), Bioinformatics and Genomic Networks (7 papers), Opinion Dynamics and Social Influence (5 papers), Microbial Metabolic Engineering and Bioproduction (4 papers), Theoretical and Computational Physics (3 papers), Advanced Image Fusion Techniques (2 papers) and Stochastic processes and statistical mechanics (2 papers). The work is most often cited by research in Computational Theory and Mathematics (154 citations), Immunology (146 citations), Computational Mathematics (3 citations), Cancer Research (65 citations) and Molecular Biology (258 citations). Zikai Wu has collaborated with scholars based in China, Japan and United States. Frequent co-authors include Luonan Chen, Xing‐Ming Zhao, Sankar Ghosh, Will Liao, Hyun-Ju Oh, Yenkel Grinberg‐Bleyer, Nicole Heise, Pingzhang Wang, Roland M. Schmid and Jiguang Wang. Their work appears in journals such as BMC Systems Biology, IET Systems Biology, Neurocomputing, Planta and Molecules and Cells.
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.