Kaida Wu
Impact in
- Hematology top 2%
- Multiple Myeloma Research and Treatments
- Acute Myeloid Leukemia Research
- Genetics top 10%
Papers in
- Hematology 20
- Multiple Myeloma Research and Treatments 15
- Acute Myeloid Leukemia Research 4
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- Cancer therapeutics and mechanisms 5
- Co-authors
- Malcolm A.S. Moore (13 shared papers)Selina Chen‐Kiang (5 shared papers)Karen Bang (3 shared papers)Marianne Lund (3 shared papers)Kristian Thestrup‐Pedersen (5 shared papers)Rachel A. Gottschalk (2 shared papers)Scott Ely (3 shared papers)Peter L. Toogood (2 shared papers)
- Journals
- Blood (14 papers)Journal of Clinical Oncology (4 papers)Cancer Research (4 papers)Acta Dermato Venereologica (2 papers)Experimental Dermatology (1 paper)
- Partner nations
- United StatesFranceSpain
In The Last Decade
Kaida Wu
37 papers receiving 916 citations
Peers
Comparison fields: 5 of 63
- Hematology 376
- Genetics 114
- Oncology 233
- Physiology 186
- Molecular Biology 458
Countries citing papers authored by Kaida Wu
This map shows the geographic impact of Kaida 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 Kaida Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaida Wu more than expected).
Fields of papers citing papers by Kaida Wu
This network shows the impact of papers produced by Kaida 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 Kaida Wu. The network helps show where Kaida Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Kaida 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 37 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 171 | |
| 2 | 2003 | 99 | |
| 3 | 2003 | 85 | |
| 4 | 2000 | 72 | |
| 5 | 2003 | 59 | |
| 6 | 2006 | 45 | |
| 7 | 2004 | 37 | |
| 8 | 1999 | 32 | |
| 9 | 2012 | 30 | |
| 10 | 2017 | 29 | |
| 11 | 2005 | 28 | |
| 12 | 2017 | 28 | |
| 13 | 2016 | 26 | |
| 14 | 2017 | 23 | |
| 15 | 2016 | 22 | |
| 16 | 2014 | 18 | |
| 17 | 2017 | 16 | |
| 18 | 1999 | 16 | |
| 19 | 2017 | 15 | |
| 20 | 2006 | 11 |
About Kaida Wu
Kaida Wu is a scholar working on Hematology, Molecular Biology, Oncology, Genetics and Physiology, having authored 37 papers that have together received 927 indexed citations. Recurring topics across this work include Multiple Myeloma Research and Treatments (15 papers), Telomeres, Telomerase, and Senescence (8 papers), Cancer Treatment and Pharmacology (5 papers), Cancer therapeutics and mechanisms (5 papers), Acute Myeloid Leukemia Research (4 papers), Immunotherapy and Immune Responses (4 papers), Chronic Lymphocytic Leukemia Research (4 papers) and Microtubule and mitosis dynamics (3 papers). The work is most often cited by research in Hematology (376 citations), Genetics (114 citations), Oncology (233 citations), Physiology (186 citations) and Molecular Biology (458 citations). Kaida Wu has collaborated with scholars based in United States, France and Spain. Frequent co-authors include Malcolm A.S. Moore, Selina Chen‐Kiang, Karen Bang, Marianne Lund, Kristian Thestrup‐Pedersen, Rachel A. Gottschalk, Scott Ely, Peter L. Toogood, Rubén Niesvizky and Raymond L. Comenzo. Their work appears in journals such as Blood, Journal of Clinical Oncology, Cancer Research, Acta Dermato Venereologica and Experimental Dermatology.
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.