Maya Dajee
- Hematology top 5%
- Multiple Myeloma Research and Treatments 3
- Cancer Research top 5%
- NF-κB Signaling Pathways 4
- Oncology top 5%
- Cytokine Signaling Pathways and Interactions 3
- Immunology top 5%
- Reproductive System and Pregnancy 4
- Molecular Biology top 10%
- Protein Degradation and Inhibitors 3
- Melanoma and MAPK Pathways 3
- Protein Kinase Regulation and GTPase Signaling 3
- Ubiquitin and proteasome pathways 3
- Co-authors
- Ti CaiPaul A. KhavariChristopher J. KirkCheryl L. GreenQun LinYoshiaki KuboMirella LazarovShiying Tao
- Cited by
- HematologyCancer ResearchOncology
- Partner nations
- United StatesGermanySwitzerland
In The Last Decade
Maya Dajee
22 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 92
- Hematology 305
- Cancer Research 354
- Oncology 628
- Immunology 434
- Molecular Biology 1.2k
Countries citing papers authored by Maya Dajee
This map shows the geographic impact of Maya Dajee'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 Maya Dajee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Dajee more than expected).
Fields of papers citing papers by Maya Dajee
This network shows the impact of papers produced by Maya Dajee. 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 Maya Dajee. The network helps show where Maya Dajee may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Maya Dajee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2018 | 2 | |
| 3 | 2017 | 17 | |
| 4 | 2016 | 12 | |
| 5 | 2010 | 207 | |
| 6 | 2008 | 1 | |
| 7 | 2008 | 4 | |
| 8 | 2008 | 10 | |
| 9 | Antitumor Activity of PR-171, a Novel Irreversible Inhibitor of the Proteasomebreakdown → | 2007 | 542 |
| 10 | 2006 | 44 | |
| 11 | 2006 | 40 | |
| 12 | 2004 | 36 | |
| 13 | 2003 | 448 | |
| 14 | 2002 | 160 | |
| 15 | 2002 | 82 | |
| 16 | 1998 | 189 | |
| 17 | 1998 | 28 | |
| 18 | 1996 | 42 | |
| 19 | 1996 | 53 | |
| 20 | 1996 | 15 |
About Maya Dajee
Maya Dajee is a scholar working on Cancer Research, Oncology, Immunology, Hematology and Agronomy and Crop Science, having authored 23 papers that have together received 2.0k indexed citations. Recurring topics across this work include Reproductive System and Pregnancy (4 papers), NF-κB Signaling Pathways (4 papers), Multiple Myeloma Research and Treatments (3 papers), Protein Degradation and Inhibitors (3 papers), Melanoma and MAPK Pathways (3 papers), Protein Kinase Regulation and GTPase Signaling (3 papers), Ubiquitin and proteasome pathways (3 papers) and Cytokine Signaling Pathways and Interactions (3 papers). The work is most often cited by research in Hematology (305 citations), Cancer Research (354 citations), Oncology (628 citations), Immunology (434 citations) and Molecular Biology (1.2k citations). Maya Dajee has collaborated with scholars based in United States, Germany and Switzerland. Frequent co-authors include Ti Cai, Paul A. Khavari, Christopher J. Kirk, Cheryl L. Green, Qun Lin, Yoshiaki Kubo, Mirella Lazarov, Shiying Tao, Masahito Tarutani and Darryl L. Russell. Their work appears in journals such as Molecular Endocrinology, Cancer Research, Blood, Circulation Journal and Nature Medicine.
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.