Dea Nagy
Impact in
Papers in
-
- RNA Interference and Gene Delivery 6
- CRISPR and Genetic Engineering 4
- Viral Infectious Diseases and Gene Expression in Insects 2
- Genetics 9
- Virus-based gene therapy research 9
- Co-authors
- Didier Trono (1 shared paper)Romain Zufferey (1 shared paper)Luigi Naldini (1 shared paper)Olivier Danos (2 shared papers)Richard O. Snyder (2 shared papers)L. Cohen (2 shared papers)Joseph Vargas (5 shared papers)G. Patijn (1 shared paper)
- Journals
- Blood (4 papers)Cell Transplantation (2 papers)The Prostate (1 paper)Genomics (1 paper)Advances in pharmacology (1 paper)
- Partner nations
- United StatesCanadaHungary
In The Last Decade
Dea Nagy
22 papers receiving 3.3k citations
Dea Nagy's Hit Papers
Peers
Comparison fields: 5 of 95
- Genetics 1.9k
- Virology 236
- Molecular Biology 2.1k
- Oncology 590
- Hematology 206
Countries citing papers authored by Dea Nagy
This map shows the geographic impact of Dea Nagy'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 Dea Nagy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dea Nagy more than expected).
Fields of papers citing papers by Dea Nagy
This network shows the impact of papers produced by Dea Nagy. 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 Dea Nagy. The network helps show where Dea Nagy may publish in the future.
Co-authors
The 25 scholars most cited alongside Dea Nagy, 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 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Multiply attenuated lentiviral vector achieves efficient gene delivery in vivo Hit paper breakdown → | 1997 | 1641 |
| 2 | Persistent and therapeutic concentrations of human factor IX in mice after hepatic gene transfer of recombinant AAV vectors Hit paper breakdown → | 1997 | 521 |
| 3 | 2006 | 286 | |
| 4 | 2010 | 274 | |
| 5 | 2006 | 174 | |
| 6 | 1997 | 98 | |
| 7 | 2003 | 85 | |
| 8 | 2000 | 78 | |
| 9 | 2010 | 60 | |
| 10 | 2003 | 57 | |
| 11 | 1977 | 49 | |
| 12 | 1997 | 48 | |
| 13 | 2001 | 29 | |
| 14 | 2013 | 18 | |
| 15 | 2000 | 15 | |
| 16 | 1997 | 10 | |
| 17 | 1985 | 6 | |
| 18 | 2001 | 3 | |
| 19 | 1999 | 2 | |
| 20 | 2013 | 1 |
About Dea Nagy
Dea Nagy is a scholar working on Molecular Biology, Genetics, Oncology, Neurology and Pulmonary and Respiratory Medicine, having authored 22 papers that have together received 3.5k indexed citations. Recurring topics across this work include Virus-based gene therapy research (9 papers), RNA Interference and Gene Delivery (6 papers), CAR-T cell therapy research (4 papers), CRISPR and Genetic Engineering (4 papers), Prostate Cancer Treatment and Research (3 papers), Viral Infectious Diseases and Gene Expression in Insects (2 papers), Neurological disorders and treatments (2 papers) and Radiopharmaceutical Chemistry and Applications (2 papers). The work is most often cited by research in Genetics (1.9k citations), Virology (236 citations), Molecular Biology (2.1k citations), Oncology (590 citations) and Hematology (206 citations). Dea Nagy has collaborated with scholars based in United States, Canada and Hungary. Frequent co-authors include Didier Trono, Romain Zufferey, Luigi Naldini, Olivier Danos, Richard O. Snyder, L. Cohen, Joseph Vargas, G. Patijn, Tongyao Liu and Carol H. Miao. Their work appears in journals such as Blood, Cell Transplantation, The Prostate, Genomics and Advances in pharmacology.
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.