Kui Nie
Impact in
- Cancer Research top 10%
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
- Cancer Genomics and Diagnostics
- Parasitology top 5%
Papers in
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- Lymphoma Diagnosis and Treatment 13
-
- Glycosylation and Glycoproteins Research 3
- Cancer-related gene regulation 3
- Co-authors
- Wayne Tam (14 shared papers)Daniel M. Knowles (6 shared papers)David Redmond (7 shared papers)Olivier Elemento (7 shared papers)Zuoyong Zhou (6 shared papers)Shijun Hu (6 shared papers)Ari Melnick (6 shared papers)Yanwen Jiang (5 shared papers)
- Journals
- Blood (5 papers)Journal of Visualized Experiments (2 papers)Experimental Parasitology (2 papers)American Journal Of Pathology (2 papers)American Journal of Clinical Pathology (2 papers)
- Partner nations
- ChinaUnited StatesItaly
In The Last Decade
Kui Nie
29 papers receiving 671 citations
Peers
Comparison fields: 5 of 72
- Cancer Research 200
- Parasitology 75
- Pathology and Forensic Medicine 206
- Genetics 93
- Animal Science and Zoology 84
Countries citing papers authored by Kui Nie
This map shows the geographic impact of Kui Nie'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 Kui Nie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kui Nie more than expected).
Fields of papers citing papers by Kui Nie
This network shows the impact of papers produced by Kui Nie. 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 Kui Nie. The network helps show where Kui Nie may publish in the future.
Co-authors
The 25 scholars most cited alongside Kui Nie, 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 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2008 | 133 | |
| 2 | 2015 | 94 | |
| 3 | 2014 | 64 | |
| 4 | 2010 | 50 | |
| 5 | 2017 | 45 | |
| 6 | 2013 | 44 | |
| 7 | 2014 | 44 | |
| 8 | 2010 | 36 | |
| 9 | 2010 | 31 | |
| 10 | 2021 | 24 | |
| 11 | 2012 | 15 | |
| 12 | 2015 | 14 | |
| 13 | 2009 | 12 | |
| 14 | 2017 | 11 | |
| 15 | 2018 | 10 | |
| 16 | 2011 | 9 | |
| 17 | 2014 | 8 | |
| 18 | 2015 | 8 | |
| 19 | 2023 | 7 | |
| 20 | 2024 | 3 |
About Kui Nie
Kui Nie is a scholar working on Pathology and Forensic Medicine, Molecular Biology, Animal Science and Zoology, Small Animals and Electrical and Electronic Engineering, having authored 30 papers that have together received 674 indexed citations. Recurring topics across this work include Lymphoma Diagnosis and Treatment (13 papers), Coccidia and coccidiosis research (7 papers), Veterinary medicine and infectious diseases (5 papers), Animal Nutrition and Physiology (4 papers), Glycosylation and Glycoproteins Research (3 papers), Cancer-related gene regulation (3 papers), Viral-associated cancers and disorders (2 papers) and Chronic Lymphocytic Leukemia Research (2 papers). The work is most often cited by research in Cancer Research (200 citations), Parasitology (75 citations), Pathology and Forensic Medicine (206 citations), Genetics (93 citations) and Animal Science and Zoology (84 citations). Kui Nie has collaborated with scholars based in China, United States and Italy. Frequent co-authors include Wayne Tam, Daniel M. Knowles, David Redmond, Olivier Elemento, Zuoyong Zhou, Shijun Hu, Ari Melnick, Yanwen Jiang, Leonard Tan and Amy Chadburn. Their work appears in journals such as Blood, Journal of Visualized Experiments, Experimental Parasitology, American Journal Of Pathology and American Journal of Clinical Pathology.
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.