Peng Ni
Impact in
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- Computational Drug Discovery Methods
- Rough Sets and Fuzzy Logic
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- Genomics and Phylogenetic Studies
- RNA modifications and cancer
- Bioinformatics and Genomic Networks
- RNA and protein synthesis mechanisms
Papers in
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- Genomics and Phylogenetic Studies 7
- Machine Learning in Bioinformatics 6
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- Plant Virus Research Studies 10
- Co-authors
- Jianxin Wang (19 shared papers)Feng Luo (10 shared papers)Neng Huang (9 shared papers)C. Cheng Kao (5 shared papers)Fang‐Xiang Wu (4 shared papers)Yi Pan (4 shared papers)Chuan‐Le Xiao (5 shared papers)Bogdan Dragnea (5 shared papers)
- Journals
- IEEE/ACM Transactions on Computational Biology and Bioinformatics (6 papers)Bioinformatics (5 papers)Nature Communications (4 papers)Journal of Molecular Biology (3 papers)Information Sciences (2 papers)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Peng Ni
46 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 120
- Computational Theory and Mathematics 219
- Molecular Biology 557
- Ecology 202
- Plant Science 273
- Endocrinology 36
Countries citing papers authored by Peng Ni
This map shows the geographic impact of Peng Ni'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 Peng Ni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peng Ni more than expected).
Fields of papers citing papers by Peng Ni
This network shows the impact of papers produced by Peng Ni. 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 Peng Ni. The network helps show where Peng Ni may publish in the future.
Co-authors
The 25 scholars most cited alongside Peng Ni, 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 46 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 170 | |
| 2 | 2023 | 91 | |
| 3 | 2012 | 72 | |
| 4 | 2021 | 69 | |
| 5 | 2017 | 66 | |
| 6 | 2018 | 57 | |
| 7 | 2020 | 57 | |
| 8 | 2023 | 52 | |
| 9 | 2017 | 48 | |
| 10 | 2014 | 46 | |
| 11 | 2013 | 41 | |
| 12 | 2013 | 38 | |
| 13 | 2019 | 37 | |
| 14 | 2018 | 35 | |
| 15 | 2010 | 33 | |
| 16 | 2010 | 26 | |
| 17 | 2016 | 22 | |
| 18 | 2021 | 22 | |
| 19 | 2020 | 21 | |
| 20 | 2024 | 19 |
About Peng Ni
Peng Ni is a scholar working on Molecular Biology, Plant Science, Computational Theory and Mathematics, Ecology and Artificial Intelligence, having authored 46 papers that have together received 1.2k indexed citations. Recurring topics across this work include Plant Virus Research Studies (10 papers), Bacteriophages and microbial interactions (8 papers), Genomics and Phylogenetic Studies (7 papers), Computational Drug Discovery Methods (6 papers), Machine Learning in Bioinformatics (6 papers), Viral gastroenteritis research and epidemiology (4 papers), Text and Document Classification Technologies (3 papers) and Rough Sets and Fuzzy Logic (3 papers). The work is most often cited by research in Computational Theory and Mathematics (219 citations), Molecular Biology (557 citations), Ecology (202 citations), Plant Science (273 citations) and Endocrinology (36 citations). Peng Ni has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Jianxin Wang, Feng Luo, Neng Huang, C. Cheng Kao, Fang‐Xiang Wu, Yi Pan, Chuan‐Le Xiao, Bogdan Dragnea, Robert C. Vaughan and Cuiping Li. Their work appears in journals such as IEEE/ACM Transactions on Computational Biology and Bioinformatics, Bioinformatics, Nature Communications, Journal of Molecular Biology and Information Sciences.
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.