David S. Siegel
- Hematology top 0.01%
- Multiple Myeloma Research and Treatments 322
- Oncology top 0.1%
- Cancer Treatment and Pharmacology 77
- Peptidase Inhibition and Analysis 56
- CAR-T cell therapy research 31
- Genetics top 0.5%
- Chronic Lymphocytic Leukemia Research 37
- Molecular Biology top 0.2%
- Protein Degradation and Inhibitors 173
- Histone Deacetylase Inhibitors Research 40
- Ubiquitin and proteasome pathways 32
- Toxicology top 0.5%
- Co-authors
- Bart BarlogieNikhil C. MunshiSundar JagannathSeema SinghalS. Vincent RajkumarJerome B. ZeldisRubén NiesvizkyJayesh Mehta
- Cited by
- HematologyOncologyGenetics
- Journals
- New England Journal of Medicine (2 papers)Proceedings of the National Academy of Sciences (1 paper)The Lancet (1 paper)
- Partner nations
- United StatesCanadaSpain
In The Last Decade
David S. Siegel
393 papers receiving 15.4k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Hematology 11.3k
- Oncology 7.6k
- Genetics 1.6k
- Molecular Biology 10.1k
- Toxicology 260
Countries citing papers authored by David S. Siegel
This map shows the geographic impact of David S. Siegel'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 David S. Siegel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David S. Siegel more than expected).
Fields of papers citing papers by David S. Siegel
This network shows the impact of papers produced by David S. Siegel. 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 David S. Siegel. The network helps show where David S. Siegel may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David S. Siegel, 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 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 2 | |
| 7 | 2023 | 1 | |
| 8 | Preferences and Priorities for Relapsed Multiple Myeloma Treatments Among Patients and Caregivers in the United States | 2022 | 9 |
| 9 | 2022 | 4 | |
| 10 | 2022 | 1 | |
| 11 | 2021 | 2 | |
| 12 | 2021 | 2 | |
| 13 | 2021 | 4 | |
| 14 | 2020 | 27 | |
| 15 | 2019 | 39 | |
| 16 | 2019 | 63 | |
| 17 | 2017 | 0 | |
| 18 | 2017 | 2 | |
| 19 | 2013 | 36 | |
| 20 | Barriers to and Strategies for Effective Blood Pressure Control | 2005 | 3 |
About David S. Siegel
David S. Siegel is a scholar working on Hematology, Oncology and Genetics, having authored 415 papers that have together received 15.8k indexed citations. Recurring topics across this work include Multiple Myeloma Research and Treatments (322 papers), Protein Degradation and Inhibitors (173 papers), Cancer Treatment and Pharmacology (77 papers), Peptidase Inhibition and Analysis (56 papers), Histone Deacetylase Inhibitors Research (40 papers), Chronic Lymphocytic Leukemia Research (37 papers), Ubiquitin and proteasome pathways (32 papers) and CAR-T cell therapy research (31 papers). The work is most often cited by research in Hematology (11.3k citations), Oncology (7.6k citations) and Genetics (1.6k citations). David S. Siegel has collaborated with scholars based in United States, Canada and Spain. Frequent co-authors include Bart Barlogie, Nikhil C. Munshi, Sundar Jagannath, Seema Singhal, S. Vincent Rajkumar, Jerome B. Zeldis, Rubén Niesvizky, Jayesh Mehta, John Crowley and David H. Vesole. Their work appears in journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and The Lancet.
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.