Greg Ahmann

2.2k total citations
18 papers, 541 citations indexed

About

Greg Ahmann is a scholar working on Hematology, Molecular Biology and Oncology. According to data from OpenAlex, Greg Ahmann has authored 18 papers receiving a total of 541 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Hematology, 13 papers in Molecular Biology and 7 papers in Oncology. Recurrent topics in Greg Ahmann's work include Multiple Myeloma Research and Treatments (16 papers), Protein Degradation and Inhibitors (11 papers) and Glycosylation and Glycoproteins Research (2 papers). Greg Ahmann is often cited by papers focused on Multiple Myeloma Research and Treatments (16 papers), Protein Degradation and Inhibitors (11 papers) and Glycosylation and Glycoproteins Research (2 papers). Greg Ahmann collaborates with scholars based in United States, Singapore and Germany. Greg Ahmann's co-authors include Rafaël Fonseca, S. Vincent Rajkumar, Shaji Kumar, Philip R. Greipp, Martha Q. Lacy, Wee Joo Chng, Robert A. Kyle, Morie A. Gertz, Angela Dispenzieri and Tammy Price-Troska and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and Cancer Research.

In The Last Decade

Greg Ahmann

17 papers receiving 533 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Greg Ahmann United States 9 435 410 179 56 50 18 541
Jessica Haug United States 10 303 0.7× 244 0.6× 141 0.8× 57 1.0× 21 0.4× 23 423
Madhumita Santra United States 6 426 1.0× 432 1.1× 195 1.1× 41 0.7× 13 0.3× 9 532
Divyamani Srinivasan United States 6 252 0.6× 169 0.4× 109 0.6× 24 0.4× 39 0.8× 7 390
Santiago Barrio Spain 13 359 0.8× 320 0.8× 169 0.9× 84 1.5× 11 0.2× 37 521
R.K.M. Protheroe United Kingdom 9 670 1.5× 674 1.6× 286 1.6× 71 1.3× 14 0.3× 12 818
J Ackermann Austria 12 280 0.6× 287 0.7× 185 1.0× 85 1.5× 15 0.3× 17 445
Ana Gabrea United States 8 540 1.2× 483 1.2× 241 1.3× 133 2.4× 19 0.4× 9 732
Naozo Nakazawa Japan 8 238 0.5× 230 0.6× 94 0.5× 52 0.9× 29 0.6× 11 361
Donata Verdelli Italy 10 398 0.9× 346 0.8× 145 0.8× 75 1.3× 7 0.1× 14 532
Young Trieu Canada 13 561 1.3× 525 1.3× 335 1.9× 79 1.4× 13 0.3× 28 728

Countries citing papers authored by Greg Ahmann

Since Specialization
Citations

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

Fields of papers citing papers by Greg Ahmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Greg Ahmann

This figure shows the co-authorship network connecting the top 25 collaborators of Greg Ahmann. A scholar is included among the top collaborators of Greg Ahmann 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 Greg Ahmann. Greg Ahmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Ahmann, Greg, et al.. (2025). Sex-specific dysregulation of exosomal non-coding RNAs drives multiple myeloma progression. Blood Cancer Journal. 15(1). 162–162.
2.
Zhu, Yuan Xiao, Laura A. Bruins, Cecília Bonolo de Campos, et al.. (2025). Exploring BCL2 regulation and upstream signaling transduction in venetoclax resistance in multiple myeloma: potential avenues for therapeutic intervention. Blood Cancer Journal. 15(1). 10–10. 3 indexed citations
3.
Zhu, Yuan Xiao, Laura A. Bruins, Xianfeng Chen, et al.. (2022). Transcriptional profiles define drug refractory disease in myeloma. SHILAP Revista de lepidopterología. 3(3). 804–814. 1 indexed citations
4.
Campos, Cecília Bonolo de, Nathalie Meurice, Laura A. Bruins, et al.. (2019). Ex vivo sensitivity to venetoclax is predictive of clinical activity. Clinical Lymphoma Myeloma & Leukemia. 19(10). e114–e114. 1 indexed citations
5.
White, Brian S., Irena Lanc, Julie O’Neal, et al.. (2018). A multiple myeloma-specific capture sequencing platform discovers novel translocations and frequent, risk-associated point mutations in IGLL5. Blood Cancer Journal. 8(3). 35–35. 35 indexed citations
6.
Colón‐Otero, Gerardo, Scott A. Van Wier, Greg Ahmann, et al.. (2016). Prevalence of BCL-2/J(H) Translocation in Healthy African Americans. Annals of Hematology. 96(1). 51–55. 1 indexed citations
7.
Sebastian, Sinto, Yuan Xiao Zhu, Esteban Braggio, et al.. (2016). Multiple myeloma cells’ capacity to decompose H2O2 determines lenalidomide sensitivity. Blood. 129(8). 991–1007. 31 indexed citations
8.
Kortüm, K. Martin, Christian Langer, Jorge Monge, et al.. (2015). Longitudinal analysis of 25 sequential sample-pairs using a custom multiple myeloma mutation sequencing panel (M3P). Annals of Hematology. 94(7). 1205–1211. 36 indexed citations
9.
Kortüm, K. Martin, Nur Hafzan Md Hanafiah, Santiago Barrio, et al.. (2015). Targeted sequencing of relapsed/refractory myeloma patients identifies an enrichment of mutations in MAPK and Cereblon pathways. Clinical Lymphoma Myeloma & Leukemia. 15. e83–e83. 1 indexed citations
10.
Schuster, Steven R., K. Martin Kortuem, Yuan Xiao Zhu, et al.. (2013). The clinical significance of cereblon expression in multiple myeloma. Leukemia Research. 38(1). 23–28. 85 indexed citations
11.
Jacobus, Susanna, Shaji Kumar, Hajime Uno, et al.. (2011). Impact of high‐risk classification by FISH: an Eastern Cooperative Oncology Group (ECOG) study E4A03. British Journal of Haematology. 155(3). 340–348. 23 indexed citations
12.
Kumar, Shaji, Hajime Uno, Susanna Jacobus, et al.. (2011). Impact of gene expression profiling-based risk stratification in patients with myeloma receiving initial therapy with lenalidomide and dexamethasone. Blood. 118(16). 4359–4362. 24 indexed citations
13.
Wier, Scott Van, Esteban Braggio, Jonathan J. Keats, et al.. (2008). Loss of p53 Is a Marker of Progression in Plasma Cell Neoplasias and Is a Negative Prognostic Factor in Relapsed Disease.. Blood. 112(11). 1663–1663. 3 indexed citations
14.
Chng, Wee Joo, Shaji Kumar, Scott VanWier, et al.. (2007). Molecular Dissection of Hyperdiploid Multiple Myeloma by Gene Expression Profiling. Cancer Research. 67(7). 2982–2989. 195 indexed citations
15.
Chng, Wee Joo, Marta Chesi, Tammy Price-Troska, et al.. (2007). Activation of MYC Pathway Is a Unifying Pathological Event in the Progression from Monoclonal Gammopathy of Undetermined Significance (MGUS) to Myeloma (MM).. Blood. 110(11). 241–241. 1 indexed citations
16.
Chng, Wee Joo, Philip R. Greipp, S. M. Jalal, et al.. (2005). Ploidy status rarely changes in myeloma patients at disease progression. Leukemia Research. 30(3). 266–271. 20 indexed citations
17.
Chng, Wee Joo, Greg Ahmann, Rafael Santana-Dávila, et al.. (2005). Clinical implication of centrosome amplification in plasma cell neoplasm. Blood. 107(9). 3669–3675. 76 indexed citations
18.

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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