Kaida Wu
Impact in
- Hematology top 2%
- Multiple Myeloma Research and Treatments
- Acute Myeloid Leukemia Research
- Genetics top 10%
Papers in
- Hematology 21
- Multiple Myeloma Research and Treatments 15
- Acute Myeloid Leukemia Research 5
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- Cancer therapeutics and mechanisms 5
- Co-authors
- Malcolm A.S. Moore (13 shared papers)Selina Chen‐Kiang (5 shared papers)Marianne Lund (3 shared papers)Karen Bang (3 shared papers)Kristian Thestrup‐Pedersen (5 shared papers)Rubén Niesvizky (3 shared papers)Raymond L. Comenzo (3 shared papers)Scott Ely (3 shared papers)
- Journals
- Blood (14 papers)Cancer Research (4 papers)Journal of Clinical Oncology (4 papers)Acta Dermato Venereologica (2 papers)Cancer (1 paper)
- Partner nations
- United StatesFranceSpain
In The Last Decade
Kaida Wu
36 papers receiving 911 citations
Peers
Comparison fields: 5 of 62
- Hematology 401
- Genetics 135
- Oncology 295
- Molecular Biology 527
- Aging 13
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 | 172 | |
| 2 | 2003 | 98 | |
| 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 | 17 | |
| 17 | 1999 | 16 | |
| 18 | 2017 | 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 922 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 (5 papers), Chronic Lymphocytic Leukemia Research (4 papers), Immunotherapy and Immune Responses (4 papers) and Fungal Infections and Studies (3 papers). The work is most often cited by research in Hematology (401 citations), Genetics (135 citations), Oncology (295 citations), Molecular Biology (527 citations) and Aging (13 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, Marianne Lund, Karen Bang, Kristian Thestrup‐Pedersen, Rubén Niesvizky, Raymond L. Comenzo, Scott Ely, Rachel A. Gottschalk and Peter L. Toogood. Their work appears in journals such as Blood, Cancer Research, Journal of Clinical Oncology, Acta Dermato Venereologica and Cancer.
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.