G. David Roodman

11.8k total citations · 2 hit papers
130 papers, 7.7k citations indexed

About

G. David Roodman is a scholar working on Oncology, Hematology and Molecular Biology. According to data from OpenAlex, G. David Roodman has authored 130 papers receiving a total of 7.7k indexed citations (citations by other indexed papers that have themselves been cited), including 91 papers in Oncology, 78 papers in Hematology and 54 papers in Molecular Biology. Recurrent topics in G. David Roodman's work include Bone health and treatments (79 papers), Multiple Myeloma Research and Treatments (78 papers) and Peptidase Inhibition and Analysis (24 papers). G. David Roodman is often cited by papers focused on Bone health and treatments (79 papers), Multiple Myeloma Research and Treatments (78 papers) and Peptidase Inhibition and Analysis (24 papers). G. David Roodman collaborates with scholars based in United States, Japan and Greece. G. David Roodman's co-authors include Jolene J. Windle, Noopur Raje, Noriyoshi Kurihara, Rebecca Silbermann, Nicola Giuliani, Sakamuri V. Reddy, Evangelos Terpos, Vittorio Rizzoli, Sun Jin Choi and Cheikh Menaa and has published in prestigious journals such as New England Journal of Medicine, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

G. David Roodman

127 papers receiving 7.5k citations

Hit Papers

Mechanisms of Bone Metast... 2004 2026 2011 2018 2004 2018 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
G. David Roodman United States 45 5.3k 3.1k 2.5k 899 893 130 7.7k
Andrew R. Belch Canada 49 4.4k 0.8× 3.9k 1.2× 4.1k 1.7× 650 0.7× 376 0.4× 218 8.9k
Nicola Giuliani Italy 48 3.3k 0.6× 3.1k 1.0× 3.4k 1.4× 219 0.2× 457 0.5× 199 6.8k
Ildiko Sarosi United States 22 4.4k 0.8× 5.5k 1.8× 519 0.2× 510 0.6× 1.4k 1.6× 30 8.8k
William C. Dougall United States 45 6.5k 1.2× 6.6k 2.1× 454 0.2× 728 0.8× 1.1k 1.2× 94 10.5k
Carmelo Carlo‐Stella Italy 40 3.0k 0.6× 2.4k 0.8× 2.4k 1.0× 567 0.6× 106 0.1× 350 9.1k
Gwyneth Van United States 28 3.3k 0.6× 4.3k 1.4× 494 0.2× 310 0.3× 1.2k 1.3× 42 6.9k
Laura M. Calvi United States 32 1.6k 0.3× 2.1k 0.7× 2.9k 1.2× 447 0.5× 367 0.4× 103 5.9k
Gabri van der Pluijm Netherlands 48 3.5k 0.7× 3.5k 1.1× 214 0.1× 1.3k 1.4× 554 0.6× 129 7.0k
Luís Costa Portugal 38 3.9k 0.7× 1.5k 0.5× 261 0.1× 1.5k 1.7× 813 0.9× 181 5.7k
Francis Y. Lee United States 39 1.7k 0.3× 2.4k 0.8× 3.5k 1.4× 839 0.9× 121 0.1× 103 7.4k

Countries citing papers authored by G. David Roodman

Since Specialization
Citations

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

Fields of papers citing papers by G. David Roodman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of G. David Roodman

This figure shows the co-authorship network connecting the top 25 collaborators of G. David Roodman. A scholar is included among the top collaborators of G. David Roodman 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 G. David Roodman. G. David Roodman 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.
Miyagawa, Kazuaki, Patrick L. Mulcrone, Jesús Delgado‐Calle, et al.. (2023). Osteoclast-derived IGF1 induces RANKL production in osteocytes and contributes to pagetic lesion formation. JCI Insight. 8(14). 6 indexed citations
2.
Marino, Silvia, Daniela N. Petrusca, Ryan T. Bishop, et al.. (2023). Pharmacologic targeting of the p62 ZZ domain enhances both anti-tumor and bone-anabolic effects of bortezomib in multiple myeloma. Haematologica. 109(5). 1501–1513. 2 indexed citations
3.
Adhikari, Manish, Tânia Amorim, Kevin McAndrews, et al.. (2021). Targeting Notch Inhibitors to the Myeloma Bone Marrow Niche Decreases Tumor Growth and Bone Destruction without Gut Toxicity. Cancer Research. 81(19). 5102–5114. 24 indexed citations
4.
Mulcrone, Patrick L., et al.. (2020). Osteocyte Vegf-a contributes to myeloma-associated angiogenesis and is regulated by Fgf23. PMC. 1 indexed citations
5.
Hiasa, Masahiro, Tatsuo Okui, Yohance M. Allette, et al.. (2017). Bone Pain Induced by Multiple Myeloma Is Reduced by Targeting V-ATPase and ASIC3. Cancer Research. 77(6). 1283–1295. 69 indexed citations
6.
Hiasa, Masahiro, Tatsuo Okui, Yohance M. Allette, et al.. (2017). Bone pain induced by multiple myeloma is reduced by targeting V-ATPase and ASIC3. PMC. 4 indexed citations
7.
Marino, Silvia, Daniela N. Petrusca, Denise Toscani, et al.. (2017). Inhibition of p62-ZZ Domain-Mediated Signaling Overcomes Bortezomib Resistance in Multiple Myeloma Cells Independent of Their p53 Status. Blood. 130. 4421–4421. 4 indexed citations
8.
Suvannasankha, Attaya, Rafat Abonour, Sherif S. Farag, et al.. (2017). Phase 2 Study of Carfilzomib and Bone Metabolism in Patients with Relapsed Multiple Myeloma. Blood. 130. 1826–1826. 4 indexed citations
9.
Silbermann, Rebecca & G. David Roodman. (2016). Current Controversies in the Management of Myeloma Bone Disease. Journal of Cellular Physiology. 231(11). 2374–2379. 21 indexed citations
10.
Feng, Rentian, Qin Tong, Zhaojun Xie, et al.. (2015). Targeting cannabinoid receptor‐2 pathway by phenylacetylamide suppresses the proliferation of human myeloma cells through mitotic dysregulation and cytoskeleton disruption. Molecular Carcinogenesis. 54(12). 1796–1806. 9 indexed citations
11.
Silbermann, Rebecca & G. David Roodman. (2013). Myeloma bone disease: Pathophysiology and management.. PMC. 7 indexed citations
12.
Terpos, Evangelos, G. David Roodman, & Meletios Α. Dimopoulos. (2013). Optimal use of bisphosphonates in patients with multiple myeloma. Blood. 121(17). 3325–3328. 33 indexed citations
13.
Cao, Huiling, Shibing Yu, Zhi Yao, et al.. (2010). Activating transcription factor 4 regulates osteoclast differentiation in mice. Journal of Clinical Investigation. 120(8). 2755–2766. 79 indexed citations
15.
Kurihara, Noriyoshi, Yuko Hiruma, Hua Zhou, et al.. (2006). Mutation of the sequestosome 1 (p62) gene increases osteoclastogenesis but does not induce Paget disease. Journal of Clinical Investigation. 117(1). 133–142. 90 indexed citations
16.
Giuliani, Nicola, Vittorio Rizzoli, & G. David Roodman. (2006). Multiple myeloma bone disease: pathophysiology of osteoblast inhibition. Blood. 108(13). 3992–3996. 229 indexed citations
17.
Kurihara, Noriyoshi, Tadashi Honjo, Jolene J. Windle, Jae Hyuk Shin, & G. David Roodman. (2005). Targeting p62ZIP in Marrow Stromal Cells Is Highly Effective at Inhibiting Myeloma Cell Growth and Osteoclast Formation.. Blood. 106(11). 630–630. 1 indexed citations
18.
Anderson, Kenneth C., John D. Shaughnessy, Bart Barlogie, Jean‐Luc Harousseau, & G. David Roodman. (2002). Multiple Myeloma. Hematology. 2002(1). 214–240. 51 indexed citations
19.
Choi, Sun Jin, Yasuo Oba, Yair Gazitt, et al.. (2001). Antisense inhibition of macrophage inflammatory protein 1-α blocks bone destruction in a model of myeloma bone disease. Journal of Clinical Investigation. 108(12). 1833–1841. 142 indexed citations
20.
Roodman, G. David, et al.. (1981). Stimulation of erythroid colony formation in vitro by erythropoietin immobilized on agarose-bound lectins.. PubMed. 98(5). 684–90. 9 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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