Deepu Madduri
Impact in
- Hematology top 1%
- Multiple Myeloma Research and Treatments
- Oncology top 2%
- CAR-T cell therapy research
- Peptidase Inhibition and Analysis
Papers in
- Hematology 50
- Multiple Myeloma Research and Treatments 49
- Oncology 45
- CAR-T cell therapy research 38
- Peptidase Inhibition and Analysis 5
- Co-authors
- Sundar Jagannath (63 shared papers)Samir Parekh (36 shared papers)Ajai Chari (26 shared papers)Joshua Richter (23 shared papers)Hearn Jay Cho (30 shared papers)Shambavi Richard (17 shared papers)Jesús G. Berdeja (21 shared papers)Alessandro Laganà (15 shared papers)
- Journals
- Blood (25 papers)Journal of Clinical Oncology (11 papers)Future Oncology (3 papers)HemaSphere (2 papers)JCO Precision Oncology (2 papers)
- Partner nations
- United StatesBelgiumGermany
In The Last Decade
Deepu Madduri
78 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 94
- Hematology 826
- Oncology 1.1k
- Immunology 285
- Radiology, Nuclear Medicine and Imaging 224
- Molecular Biology 658
Countries citing papers authored by Deepu Madduri
This map shows the geographic impact of Deepu Madduri'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 Deepu Madduri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepu Madduri more than expected).
Fields of papers citing papers by Deepu Madduri
This network shows the impact of papers produced by Deepu Madduri. 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 Deepu Madduri. The network helps show where Deepu Madduri may publish in the future.
Co-authors
The 25 scholars most cited alongside Deepu Madduri, 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 79 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 167 | |
| 2 | 2020 | 101 | |
| 3 | 2020 | 92 | |
| 4 | 2019 | 85 | |
| 5 | 2023 | 77 | |
| 6 | 2021 | 75 | |
| 7 | 2018 | 68 | |
| 8 | 2020 | 65 | |
| 9 | 2020 | 61 | |
| 10 | 2019 | 61 | |
| 11 | 2021 | 57 | |
| 12 | 2019 | 54 | |
| 13 | 2020 | 51 | |
| 14 | 2018 | 49 | |
| 15 | 2018 | 45 | |
| 16 | 2020 | 41 | |
| 17 | 2020 | 39 | |
| 18 | 2019 | 33 | |
| 19 | 2018 | 31 | |
| 20 | 2021 | 29 |
About Deepu Madduri
Deepu Madduri is a scholar working on Hematology, Oncology, Molecular Biology, Immunology and Genetics, having authored 79 papers that have together received 1.7k indexed citations. Recurring topics across this work include Multiple Myeloma Research and Treatments (49 papers), CAR-T cell therapy research (38 papers), Protein Degradation and Inhibitors (20 papers), Biosimilars and Bioanalytical Methods (14 papers), Monoclonal and Polyclonal Antibodies Research (7 papers), Chronic Lymphocytic Leukemia Research (6 papers), Peptidase Inhibition and Analysis (5 papers) and Immune Cell Function and Interaction (3 papers). The work is most often cited by research in Hematology (826 citations), Oncology (1.1k citations), Immunology (285 citations), Radiology, Nuclear Medicine and Imaging (224 citations) and Molecular Biology (658 citations). Deepu Madduri has collaborated with scholars based in United States, Belgium and Germany. Frequent co-authors include Sundar Jagannath, Samir Parekh, Ajai Chari, Joshua Richter, Hearn Jay Cho, Shambavi Richard, Jesús G. Berdeja, Alessandro Laganà, Enrique Zudaire and Larysa Sanchez. Their work appears in journals such as Blood, Journal of Clinical Oncology, Future Oncology, HemaSphere and JCO Precision Oncology.
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.