David J. Chung

3.1k total citations
99 papers, 1.3k citations indexed

About

David J. Chung is a scholar working on Hematology, Molecular Biology and Oncology. According to data from OpenAlex, David J. Chung has authored 99 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Hematology, 37 papers in Molecular Biology and 36 papers in Oncology. Recurrent topics in David J. Chung's work include Multiple Myeloma Research and Treatments (67 papers), Protein Degradation and Inhibitors (16 papers) and Immunotherapy and Immune Responses (14 papers). David J. Chung is often cited by papers focused on Multiple Myeloma Research and Treatments (67 papers), Protein Degradation and Inhibitors (16 papers) and Immunotherapy and Immune Responses (14 papers). David J. Chung collaborates with scholars based in United States, Sweden and Norway. David J. Chung's co-authors include James W. Young, Sergio Giralt, Alexander M. Lesokhin, Sean M. Devlin, Heather Landau, Emanuela Romano, Hani Hassoun, Marco Rossi, Jianda Yuan and Nikoletta Lendvai and has published in prestigious journals such as Science, Journal of Clinical Oncology and Blood.

In The Last Decade

David J. Chung

88 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David J. Chung United States 19 578 521 506 453 116 99 1.3k
Erin Gatza United States 13 429 0.7× 318 0.6× 201 0.4× 527 1.2× 28 0.2× 18 967
Dimitrios Tzachanis United States 15 260 0.4× 365 0.7× 457 0.9× 514 1.1× 32 0.3× 67 1.2k
Divino Deoliveira United States 10 226 0.4× 341 0.7× 257 0.5× 774 1.7× 19 0.2× 19 1.3k
Cindy Jacobs United States 20 215 0.4× 401 0.8× 291 0.6× 495 1.1× 15 0.1× 58 1.5k
Bernard Gregory United Kingdom 16 91 0.2× 375 0.7× 182 0.4× 561 1.2× 32 0.3× 18 1.2k
Antonios Bayas Germany 19 168 0.3× 216 0.4× 210 0.4× 301 0.7× 77 0.7× 52 1.2k
Pervinder Sagoo United Kingdom 16 114 0.2× 270 0.5× 228 0.5× 840 1.9× 23 0.2× 26 1.3k
Tanya M. Spektor United States 18 404 0.7× 517 1.0× 386 0.8× 129 0.3× 74 0.6× 66 1.1k
Yasuto Araki Japan 15 100 0.2× 590 1.1× 298 0.6× 683 1.5× 30 0.3× 31 1.4k
Kamila Bujko Poland 19 169 0.3× 440 0.8× 92 0.2× 364 0.8× 144 1.2× 44 951

Countries citing papers authored by David J. Chung

Since Specialization
Citations

This map shows the geographic impact of David J. Chung'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 J. Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David J. Chung more than expected).

Fields of papers citing papers by David J. Chung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David J. Chung. 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 J. Chung. The network helps show where David J. Chung may publish in the future.

Co-authorship network of co-authors of David J. Chung

This figure shows the co-authorship network connecting the top 25 collaborators of David J. Chung. A scholar is included among the top collaborators of David J. Chung based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David J. Chung. David J. Chung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Derkach, Andriy, Billel Gasmi, Theodora Anagnostou, et al.. (2023). T-Cell Exhaustion Signature Predicts Early Relapse after Autologous Stem Cell Transplant for Multiple Myeloma: BMT CTN 0702 Secondary Immune Analysis. Blood. 142(Supplement 1). 3326–3326. 1 indexed citations
2.
Devlin, Sean M., Susan DeWolf, Roni Tamari, et al.. (2023). Outcomes and Management of the SARS-CoV2 Omicron Variant in Recipients of Hematopoietic Cell Transplantation and Chimeric Antigen Receptor T Cell Therapy. Transplantation and Cellular Therapy. 30(1). 116.e1–116.e12. 7 indexed citations
3.
Shah, Gunjan L., David J. Chung, Roni Tamari, et al.. (2022). Humoral Response to COVID-19 Vaccination Given Pre-Cellular Therapy Wanes in Patients after Cellular Therapy: An Argument for Full Reimmunization. Transplantation and Cellular Therapy. 28(3). S373–S374. 1 indexed citations
4.
Chung, David J., Sneh Sharma, Susan DeWolf, et al.. (2022). Langerhans dendritic cell vaccine bearing mRNA-encoded tumor antigens induces antimyeloma immunity after autotransplant. Blood Advances. 6(5). 1547–1558. 30 indexed citations
5.
Scordo, Michael, Leah Gilbert, Jessica Flynn, et al.. (2022). Open-label pilot study of romiplostim for thrombocytopenia after autologous hematopoietic cell transplantation. Blood Advances. 7(8). 1536–1544. 4 indexed citations
6.
Landau, Heather, Elizabeth Rodríguez, Allison J. Applebaum, et al.. (2022). Pilot Trial of Homebound Hematopoietic Cell Transplantation. Transplantation and Cellular Therapy. 28(12). 832.e1–832.e7. 5 indexed citations
7.
Hultcrantz, Malin, Even H. Rustad, Venkata Yellapantula, et al.. (2022). Capture Rate of V(D)J Sequencing for Minimal Residual Disease Detection in Multiple Myeloma. Clinical Cancer Research. 28(10). 2160–2166. 1 indexed citations
8.
Tamari, Roni, Ioannis Politikos, David A. Knorr, et al.. (2021). Predictors of Humoral Response to SARS-CoV-2 Vaccination after Hematopoietic Cell Transplantation and CAR T-cell Therapy. Blood Cancer Discovery. 2(6). 577–585. 41 indexed citations
9.
Shah, Nishi, Parastoo B. Dahi, Doris M. Ponce, et al.. (2021). Hematopoietic Cell Transplantation is Feasible in Patients with Prior COVID-19 Infection. Transplantation and Cellular Therapy. 28(1). 55.e1–55.e5. 7 indexed citations
10.
Landau, Heather, Oscar Lahoud, Sean M. Devlin, et al.. (2019). Pilot Study of Bortezomib and Dexamethasone Pre- and Post-Risk-Adapted Autologous Stem Cell Transplantation in AL Amyloidosis. Biology of Blood and Marrow Transplantation. 26(1). 204–208. 11 indexed citations
11.
Pianko, Matthew J., Jessica Flynn, Sean M. Devlin, et al.. (2018). Treatment Outcomes in Monoclonal Immunoglobulin Deposition Disease (MIDD): A Two Center Experience. Blood. 132(Supplement 1). 5591–5591.
14.
Curran, Shane A., Justin A. Shyer, Sneh Sharma, et al.. (2016). Human Dendritic Cells Mitigate NK-Cell Dysfunction Mediated by Nonselective JAK1/2 Blockade. Cancer Immunology Research. 5(1). 52–60. 25 indexed citations
15.
Devlin, Sean M., Jill Vanak, Heather Landau, et al.. (2016). Upfront plerixafor plus G-CSF versus cyclophosphamide plus G-CSF for stem cell mobilization in multiple myeloma: efficacy and cost analysis study. Bone Marrow Transplantation. 51(4). 546–552. 38 indexed citations
16.
Murata, Kazunori, Brittany L. Carroll, Alexander M. Lesokhin, et al.. (2016). Treatment of multiple myeloma with monoclonal antibodies and the dilemma of false positive M-spikes in peripheral blood. Clinical Biochemistry. 51. 66–71. 51 indexed citations
17.
Lendvai, Nikoletta, Patrick Hilden, Sean M. Devlin, et al.. (2014). A phase 2 single-center study of carfilzomib 56 mg/m2 with or without low-dose dexamethasone in relapsed multiple myeloma. Blood. 124(6). 899–906. 65 indexed citations
19.
20.
Cohen, Nina, David J. Chung, Susan K. Seo, et al.. (2009). Hepatic Safety of Voriconazole after Allogeneic Hematopoietic Stem Cell Transplantation. Biology of Blood and Marrow Transplantation. 16(1). 46–52. 39 indexed citations

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.

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