Da Yang
Impact in
- Cancer Research top 1%
- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
- Cancer Genomics and Diagnostics
- Reproductive Medicine top 2%
- Ovarian cancer diagnosis and treatment
Papers in ⓘ
-
- Cancer-related molecular mechanisms research 17
- MicroRNA in disease regulation 11
- Cancer Genomics and Diagnostics 8
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- Advanced Harmonic Analysis Research 19
- Co-authors
- Anil K. Sood (14 shared papers)Ilya Shmulevich (11 shared papers)Yan Sun (4 shared papers)Kexin Chen (13 shared papers)Min Zhang (29 shared papers)Sofia Khan (2 shared papers)Kenneth R. Hess (2 shared papers)Wei Zhang (1 shared paper)
- Journals
- Bioinformatics (6 papers)Nature Communications (5 papers)Science Advances (4 papers)The FASEB Journal (4 papers)Technology in Cancer Research & Treatment (3 papers)
- Partner nations
- United StatesChinaRussia
In The Last Decade
Da Yang
112 papers receiving 3.8k citations
Peers
Comparison fields: 5 of 132
- Cancer Research 1.5k
- Reproductive Medicine 375
- Applied Mathematics 378
- Molecular Biology 2.4k
- Oncology 828
Countries citing papers authored by Da Yang
This map shows the geographic impact of Da Yang'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 Da Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Da Yang more than expected).
Fields of papers citing papers by Da Yang
This network shows the impact of papers produced by Da Yang. 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 Da Yang. The network helps show where Da Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Da Yang, 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 114 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 413 | |
| 2 | 2013 | 314 | |
| 3 | 2013 | 173 | |
| 4 | 2017 | 137 | |
| 5 | 2014 | 116 | |
| 6 | 2014 | 106 | |
| 7 | 2006 | 106 | |
| 8 | 2015 | 102 | |
| 9 | 2007 | 101 | |
| 10 | 2011 | 99 | |
| 11 | 2013 | 94 | |
| 12 | 2008 | 94 | |
| 13 | 2019 | 87 | |
| 14 | 2016 | 78 | |
| 15 | 2018 | 77 | |
| 16 | 2014 | 64 | |
| 17 | 2019 | 62 | |
| 18 | 2015 | 58 | |
| 19 | 2020 | 58 | |
| 20 | 2021 | 50 |
About Da Yang
Da Yang is a scholar working on Cancer Research, Applied Mathematics, Mathematical Physics, Molecular Biology and Oncology, having authored 114 papers that have together received 3.9k indexed citations. Recurring topics across this work include Advanced Harmonic Analysis Research (19 papers), Cancer-related molecular mechanisms research (17 papers), Bioinformatics and Genomic Networks (15 papers), RNA modifications and cancer (13 papers), MicroRNA in disease regulation (11 papers), RNA Research and Splicing (9 papers), Advanced Mathematical Physics Problems (8 papers) and Cancer Genomics and Diagnostics (8 papers). The work is most often cited by research in Cancer Research (1.5k citations), Reproductive Medicine (375 citations), Applied Mathematics (378 citations), Molecular Biology (2.4k citations) and Oncology (828 citations). Da Yang has collaborated with scholars based in United States, China and Russia. Frequent co-authors include Anil K. Sood, Ilya Shmulevich, Yan Sun, Kexin Chen, Min Zhang, Sofia Khan, Kenneth R. Hess, Wei Zhang, Limei Hu and Fengxia Xue. Their work appears in journals such as Bioinformatics, Nature Communications, Science Advances, The FASEB Journal and Technology in Cancer Research & Treatment.
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.