David Kaminetzky

1.5k total citations
20 papers, 303 citations indexed

About

David Kaminetzky is a scholar working on Hematology, Molecular Biology and Oncology. According to data from OpenAlex, David Kaminetzky has authored 20 papers receiving a total of 303 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Hematology, 8 papers in Molecular Biology and 8 papers in Oncology. Recurrent topics in David Kaminetzky's work include Multiple Myeloma Research and Treatments (11 papers), Peptidase Inhibition and Analysis (4 papers) and Protein Degradation and Inhibitors (3 papers). David Kaminetzky is often cited by papers focused on Multiple Myeloma Research and Treatments (11 papers), Peptidase Inhibition and Analysis (4 papers) and Protein Degradation and Inhibitors (3 papers). David Kaminetzky collaborates with scholars based in United States, United Kingdom and France. David Kaminetzky's co-authors include David M. Nanus, Ruoqian Shen, Makoto Sumitomo, Akira Iwase, Rong Zheng, Maria‐Magdalena Georgescu, Daniel Navarro, Jasmine Zain, Owen A. O’Connor and Kerry L. Burnstein and has published in prestigious journals such as Blood, Neurology and Cancer Cell.

In The Last Decade

David Kaminetzky

16 papers receiving 301 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 Kaminetzky United States 8 157 111 62 58 40 20 303
Pierre‐Marie Morice France 7 97 0.6× 243 2.2× 39 0.6× 35 0.6× 10 0.3× 21 382
Júlia Oto Spain 14 171 1.1× 39 0.4× 95 1.5× 41 0.7× 16 0.4× 31 404
Álvaro Fernández‐Pardo Spain 12 151 1.0× 26 0.2× 85 1.4× 34 0.6× 15 0.4× 19 322
Briony A. Cutts Australia 8 185 1.2× 63 0.6× 21 0.3× 71 1.2× 4 0.1× 22 325
Nadezhda Latysheva Norway 11 143 0.9× 20 0.2× 21 0.3× 88 1.5× 10 0.3× 18 286
Daniela Lakomy France 10 80 0.5× 45 0.4× 43 0.7× 88 1.5× 12 0.3× 27 401
Guillemette Fouquet France 12 147 0.9× 98 0.9× 18 0.3× 199 3.4× 10 0.3× 37 418
Remi Sumiyoshi Japan 11 96 0.6× 99 0.9× 49 0.8× 52 0.9× 20 0.5× 50 359
M Kuliszkiewicz-Janus Poland 11 146 0.9× 57 0.5× 26 0.4× 81 1.4× 3 0.1× 41 344
Maki Kitano United States 7 83 0.5× 52 0.5× 26 0.4× 71 1.2× 3 0.1× 8 225

Countries citing papers authored by David Kaminetzky

Since Specialization
Citations

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

Fields of papers citing papers by David Kaminetzky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Kaminetzky

This figure shows the co-authorship network connecting the top 25 collaborators of David Kaminetzky. A scholar is included among the top collaborators of David Kaminetzky 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 Kaminetzky. David Kaminetzky 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.
2.
Freeman, Daniel J., Catherine Diefenbach, Linda Lam, et al.. (2024). terraFlow, a high-parameter analysis tool, reveals T cell exhaustion and dysfunctional cytokine production in classical Hodgkin’s lymphoma. Cell Reports. 43(8). 114605–114605. 3 indexed citations
3.
Islam, Shahidul, et al.. (2024). Characterization and Comparative Outcomes of Younger Multiple Myeloma Patients. Blood. 144(Supplement 1). 5155–5155.
4.
Kaminetzky, David, et al.. (2023). Immune Checkpoint Inhibitors in Patients with Pre-existing Neurologic Autoimmune Disorders. Current Neurology and Neuroscience Reports. 23(11). 735–750. 11 indexed citations
5.
Islam, Shahidul, et al.. (2023). Young and Distinct: Clinical Characteristics and Outcomes of Patients 50 or Younger with Multiple Myeloma. Blood. 142(Supplement 1). 6684–6684.
6.
Varma, Gaurav, Jaeil Ahn, Adolfo Aleman, et al.. (2023). Teclistamab Demonstrates Clinical Activity in Real-World Patients Ineligible for the Pivotal Majestec-1 Trial. Blood. 142(Supplement 1). 4741–4741. 4 indexed citations
7.
Ashby, Cody, Eileen M. Boyle, Michael Bauer, et al.. (2022). Structural variants shape the genomic landscape and clinical outcome of multiple myeloma. Blood Cancer Journal. 12(5). 85–85. 10 indexed citations
8.
Braunstein, Marc, Louis Williams, David Kaminetzky, et al.. (2022). Inflammation and infection in plasma cell disorders: how pathogens shape the fate of patients. Leukemia. 36(3). 613–624. 17 indexed citations
9.
Boyle, Eileen M., Patrick Blaney, Yubao Wang, et al.. (2021). Unifying the Definition of High-Risk in Multiple Myeloma. Blood. 138(Supplement 1). 2714–2714.
10.
Maura, Francesco, Eileen M. Boyle, Even H Rustad, et al.. (2021). Chromothripsis as a pathogenic driver of multiple myeloma. Seminars in Cell and Developmental Biology. 123. 115–123. 25 indexed citations
11.
12.
Freeman, Dan, Linda Lam, Bruce G. Raphael, et al.. (2021). Terraflow, a New High Parameter Data Analysis Tool, Reveals Systemic T-Cell Exhaustion and Dysfunctional Cytokine Production in Classical Hodgkin Lymphoma. Blood. 138(Supplement 1). 3516–3516. 1 indexed citations
13.
Moore, William H., James S. Babb, David Kaminetzky, et al.. (2020). Pulmonary Embolism at CT Pulmonary Angiography in Patients with COVID-19. Radiology Cardiothoracic Imaging. 2(4). e200308–e200308. 48 indexed citations
14.
Boyle, Eileen M., Louis Williams, Patrick Blaney, et al.. (2020). Influence of Aging Processes on the Biology and Outcome of Multiple Myeloma. Blood. 136(Supplement 1). 8–9. 1 indexed citations
15.
Azzi, Jacques, Maher Abdul‐Hay, David Kaminetzky, et al.. (2018). Midostaurin in Combination with Idarubicin and Cytarabine (3+7) Induction for FLT3 Positive AML - Very High Complete Response Rates and Transition to Allogeneic Transplantation. Blood. 132(Supplement 1). 5216–5216. 3 indexed citations
16.
Barta, Stefan K., Rishi Jain, Jason Carter, et al.. (2017). Pharmacokinetics-directed Intravenous Busulfan Combined With High-dose Melphalan and Bortezomib as a Conditioning Regimen for Patients With Multiple Myeloma. Clinical Lymphoma Myeloma & Leukemia. 17(10). 650–657. 5 indexed citations
17.
Kister, Ilya, et al.. (2015). CNS neutrophilic vasculitis in neuro-Sweet disease. Neurology. 85(9). 829–830. 6 indexed citations
18.
Zain, Jasmine, David Kaminetzky, & Owen A. O’Connor. (2010). Emerging role of epigenetic therapies in cutaneous T-cell lymphomas. Expert Review of Hematology. 3(2). 187–203. 24 indexed citations
19.
Sumitomo, Makoto, Akira Iwase, Rong Zheng, et al.. (2004). Synergy in tumor suppression by direct interaction of Neutral Endopeptidase with PTEN. Cancer Cell. 5(1). 67–78. 93 indexed citations
20.
Shen, Ruoqian, Makoto Sumitomo, Jie Dai, et al.. (2000). Androgen-Induced Growth Inhibition of Androgen Receptor Expressing Androgen-Independent Prostate Cancer Cells Is Mediated by Increased Levels of Neutral Endopeptidase*. Endocrinology. 141(5). 1699–1704. 50 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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