Yan Cui
Impact in
- Cancer Research top 2%
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
- Aging top 5%
Papers in
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- Gene expression and cancer classification 22
- Bioinformatics and Genomic Networks 20
- RNA Research and Splicing 11
- Machine Learning in Bioinformatics 8
- Gene Regulatory Network Analysis 7
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- MicroRNA in disease regulation 16
- Cancer-related molecular mechanisms research 11
- Co-authors
- Anindya Bhattacharya (15 shared papers)Jesse D. Ziebarth (16 shared papers)Li Bao (3 shared papers)Mi Zhou (9 shared papers)Wing Hung Wong (7 shared papers)Robert W. Williams (12 shared papers)Nishchal K. Verma (11 shared papers)Xiaogang Guo (3 shared papers)
- Journals
- Nucleic Acids Research (9 papers)PLoS ONE (7 papers)Scientific Reports (4 papers)Neurocomputing (3 papers)Human Molecular Genetics (3 papers)
- Partner nations
- ChinaUnited StatesIndia
In The Last Decade
Yan Cui
132 papers receiving 3.6k citations
Peers
Comparison fields: 5 of 173
- Cancer Research 783
- Aging 66
- Molecular Biology 2.2k
- Health Informatics 33
- Genetics 518
Countries citing papers authored by Yan Cui
This map shows the geographic impact of Yan Cui'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 Yan Cui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yan Cui more than expected).
Fields of papers citing papers by Yan Cui
This network shows the impact of papers produced by Yan Cui. 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 Yan Cui. The network helps show where Yan Cui may publish in the future.
Co-authors
The 25 scholars most cited alongside Yan Cui, 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 144 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 270 | |
| 2 | 2005 | 186 | |
| 3 | 2019 | 156 | |
| 4 | 2014 | 144 | |
| 5 | 2012 | 139 | |
| 6 | 2006 | 131 | |
| 7 | 2005 | 126 | |
| 8 | 2015 | 116 | |
| 9 | 2022 | 113 | |
| 10 | 2011 | 112 | |
| 11 | 2018 | 105 | |
| 12 | 2005 | 97 | |
| 13 | 2015 | 96 | |
| 14 | 2007 | 93 | |
| 15 | 2000 | 92 | |
| 16 | 2020 | 84 | |
| 17 | 2007 | 74 | |
| 18 | 2002 | 73 | |
| 19 | 2012 | 69 | |
| 20 | 2005 | 67 |
About Yan Cui
Yan Cui is a scholar working on Molecular Biology, Cancer Research, Genetics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 144 papers that have together received 3.7k indexed citations. Recurring topics across this work include Gene expression and cancer classification (22 papers), Bioinformatics and Genomic Networks (20 papers), MicroRNA in disease regulation (16 papers), RNA Research and Splicing (11 papers), Cancer-related molecular mechanisms research (11 papers), Genetic Mapping and Diversity in Plants and Animals (9 papers), Machine Learning in Bioinformatics (8 papers) and Gene Regulatory Network Analysis (7 papers). The work is most often cited by research in Cancer Research (783 citations), Aging (66 citations), Molecular Biology (2.2k citations), Health Informatics (33 citations) and Genetics (518 citations). Yan Cui has collaborated with scholars based in China, United States and India. Frequent co-authors include Anindya Bhattacharya, Jesse D. Ziebarth, Li Bao, Mi Zhou, Wing Hung Wong, Robert W. Williams, Nishchal K. Verma, Xiaogang Guo, Zhaoying Shi and Fengqin Wang. Their work appears in journals such as Nucleic Acids Research, PLoS ONE, Scientific Reports, Neurocomputing and Human Molecular Genetics.
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.