Peter O’Gorman

3.3k total citations
72 papers, 1.8k citations indexed

About

Peter O’Gorman is a scholar working on Hematology, Molecular Biology and Oncology. According to data from OpenAlex, Peter O’Gorman has authored 72 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Hematology, 31 papers in Molecular Biology and 22 papers in Oncology. Recurrent topics in Peter O’Gorman's work include Multiple Myeloma Research and Treatments (37 papers), Protein Degradation and Inhibitors (14 papers) and Peptidase Inhibition and Analysis (13 papers). Peter O’Gorman is often cited by papers focused on Multiple Myeloma Research and Treatments (37 papers), Protein Degradation and Inhibitors (14 papers) and Peptidase Inhibition and Analysis (13 papers). Peter O’Gorman collaborates with scholars based in Ireland, United States and United Kingdom. Peter O’Gorman's co-authors include Paul Dowling, Martin Clynes, Despina Bazou, Tadhg G. Gleeson, Conor Shortt, Stephen Eustace, Abdul Hameed, Jennifer J. Brady, Kenneth C. Anderson and Shanta Patel and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Peter O’Gorman

66 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter O’Gorman Ireland 23 889 654 571 242 225 72 1.8k
Michael Medinger Switzerland 27 727 0.8× 927 1.4× 739 1.3× 119 0.5× 269 1.2× 106 2.3k
Adriana Zingone United States 23 447 0.5× 945 1.4× 430 0.8× 185 0.8× 190 0.8× 59 1.8k
Jay N. Lozier United States 27 1.1k 1.2× 926 1.4× 421 0.7× 152 0.6× 371 1.6× 65 2.6k
Joseph Michaeli United States 18 722 0.8× 912 1.4× 694 1.2× 93 0.4× 118 0.5× 55 2.0k
K. Martin Kortüm Germany 25 941 1.1× 1.0k 1.5× 901 1.6× 86 0.4× 240 1.1× 103 1.8k
Merav Leiba Israel 23 929 1.0× 824 1.3× 930 1.6× 83 0.3× 586 2.6× 77 2.0k
Dongfeng Qu United States 32 569 0.6× 1.0k 1.6× 894 1.6× 245 1.0× 372 1.7× 57 2.7k
Arnaldo Arbini United States 25 408 0.5× 905 1.4× 433 0.8× 106 0.4× 161 0.7× 55 1.9k
Robert A. Kyle United States 27 1.1k 1.2× 1.4k 2.2× 586 1.0× 155 0.6× 141 0.6× 45 2.2k
Ehsan Malek United States 15 397 0.4× 744 1.1× 538 0.9× 126 0.5× 245 1.1× 113 1.3k

Countries citing papers authored by Peter O’Gorman

Since Specialization
Citations

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

Fields of papers citing papers by Peter O’Gorman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter O’Gorman. 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 Peter O’Gorman. The network helps show where Peter O’Gorman may publish in the future.

Co-authorship network of co-authors of Peter O’Gorman

This figure shows the co-authorship network connecting the top 25 collaborators of Peter O’Gorman. A scholar is included among the top collaborators of Peter O’Gorman 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 Peter O’Gorman. Peter O’Gorman 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.
Katsenou, Angeliki, et al.. (2023). Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach. International Journal of Molecular Sciences. 24(21). 15570–15570. 5 indexed citations
3.
Bazou, Despina, Michael Henry, Paula Meleady, et al.. (2023). Proteomic and Metabolomic Analysis of Bone Marrow and Plasma from Patients with Extramedullary Multiple Myeloma Identifies Distinct Protein and Metabolite Signatures. Cancers. 15(15). 3764–3764. 4 indexed citations
4.
Miettinen, Juho J., Romika Kumari, Gunnhildur Ásta Traustadóttir, et al.. (2021). Aminopeptidase Expression in Multiple Myeloma Associates with Disease Progression and Sensitivity to Melflufen. Cancers. 13(7). 1527–1527. 39 indexed citations
5.
Lê, Giao, Jonathan Bones, Mark Coyne, et al.. (2019). Current and future biomarkers for risk-stratification and treatment personalisation in multiple myeloma. Molecular Omics. 15(1). 7–20. 8 indexed citations
6.
Bazou, Despina, et al.. (2018). Smoldering multiple myeloma: prevalence and current evidence guiding treatment decisions. PubMed. Volume 8. 21–31. 8 indexed citations
7.
8.
Ho, Matthew, Jiye Liu, Alireza Kalbasi, et al.. (2017). Blocking HDAC3 in Bone Marrow Stromal Cells Has Direct Anti-Multiple Myeloma Effect and Modulates T Cell Function. Blood. 130. 4429–4429. 2 indexed citations
9.
Henry, Michael, Justine Meiller, Annemarie Larkin, et al.. (2017). Novel panel of protein biomarkers to predict response to bortezomib-containing induction regimens in multiple myeloma patients. SHILAP Revista de lepidopterología. 8. 28–34. 17 indexed citations
10.
Richardson, Paul G., Antonio Palumbo, Stephen Schey, et al.. (2015). Pomalidomide – An Appraisal of Its Clinical Development and Role in the Treatment of Relapsed/Refractory Multiple Myeloma. European Oncology & Haematology. 11(2). 109–109.
11.
Dowling, Paul, Abdul Hameed, Justine Meiller, et al.. (2014). Identification of proteins found to be significantly altered when comparing the serum proteome from Multiple Myeloma patients with varying degrees of bone disease. BMC Genomics. 15(1). 904–904. 30 indexed citations
12.
Roche, Sandra, Louise Sewell, Justine Meiller, et al.. (2012). Development, validation and application of a sensitive LC–MS/MS method for the quantification of thalidomide in human serum, cells and cell culture medium. Journal of Chromatography B. 902. 16–26. 10 indexed citations
13.
Rajpal, Rajesh K., Paul Dowling, Justine Meiller, et al.. (2011). A novel panel of protein biomarkers for predicting response to thalidomide‐based therapy in newly diagnosed multiple myeloma patients. PROTEOMICS - CLINICAL APPLICATIONS. 5(9-10). 551–551. 3 indexed citations
14.
Dowling, Paul, Justine Meiller, Colin Clarke, et al.. (2011). A novel panel of protein biomarkers for predicting response to thalidomide‐based therapy in newly diagnosed multiple myeloma patients. PROTEOMICS. 11(8). 1391–1402. 26 indexed citations
15.
Ooi, Melissa, Patrick Hayden, Vassiliki Kotoula, et al.. (2009). Interactions of the Hdm2/p53 and Proteasome Pathways May Enhance the Antitumor Activity of Bortezomib. Clinical Cancer Research. 15(23). 7153–7160. 53 indexed citations
16.
Gleeson, Tadhg G., John M. Moriarty, Conor Shortt, et al.. (2008). Accuracy of whole-body low-dose multidetector CT (WBLDCT) versus skeletal survey in the detection of myelomatous lesions, and correlation of disease distribution with whole-body MRI (WBMRI). Skeletal Radiology. 38(3). 225–236. 118 indexed citations
17.
Keane, Colm, Michael Colreavy, Maureen Lynch, & Peter O’Gorman. (2008). Aspergillus flavus sinusitis in ALL. American Journal of Hematology. 84(2). 123–123. 1 indexed citations
18.
19.
Majumdar, Sisir K., G.K. Shaw, Peter O’Gorman, & Allan D. Thomson. (1982). The effect of naftidrofuryl on ethanol-induced liver damage in chronic alcoholic patients. Drug and Alcohol Dependence. 10(2-3). 135–142. 3 indexed citations
20.
O’Gorman, Peter, et al.. (1963). Sickle-cell Haemoglobin K Disease. BMJ. 2(5369). 1381–1382. 8 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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