Stephen Tirrell

1.0k total citations
16 papers, 443 citations indexed

About

Stephen Tirrell is a scholar working on Molecular Biology, Hematology and Cancer Research. According to data from OpenAlex, Stephen Tirrell has authored 16 papers receiving a total of 443 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 5 papers in Hematology and 3 papers in Cancer Research. Recurrent topics in Stephen Tirrell's work include Ubiquitin and proteasome pathways (9 papers), Protein Degradation and Inhibitors (7 papers) and Multiple Myeloma Research and Treatments (4 papers). Stephen Tirrell is often cited by papers focused on Ubiquitin and proteasome pathways (9 papers), Protein Degradation and Inhibitors (7 papers) and Multiple Myeloma Research and Treatments (4 papers). Stephen Tirrell collaborates with scholars based in United States, Canada and China. Stephen Tirrell's co-authors include Allison Berger, Robert Z. Orlowski, Kevin R. Kelly, Hélène M. Faessel, Bruce J. Dezube, Andrzej Jakubowiak, George Mulligan, Sagar Lonial, Catherine Diefenbach and Mitchell R. Smith and has published in prestigious journals such as Journal of Clinical Oncology, Blood and PLoS ONE.

In The Last Decade

Stephen Tirrell

16 papers receiving 440 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stephen Tirrell United States 9 350 132 93 77 52 16 443
Jeannette Gogel Germany 7 371 1.1× 149 1.1× 46 0.5× 109 1.4× 60 1.2× 8 471
Thang Van Nguyen United States 9 477 1.4× 194 1.5× 78 0.8× 63 0.8× 35 0.7× 12 581
Chiel Maas Netherlands 13 342 1.0× 104 0.8× 66 0.7× 50 0.6× 51 1.0× 14 444
Sreedhar Venkannagari United States 6 341 1.0× 97 0.7× 36 0.4× 115 1.5× 57 1.1× 8 415
Sari Kurki Finland 8 555 1.6× 307 2.3× 55 0.6× 101 1.3× 69 1.3× 8 714
Harish Potu United States 10 364 1.0× 201 1.5× 56 0.6× 32 0.4× 69 1.3× 17 439
Pravina Fernandez United States 4 514 1.5× 79 0.6× 43 0.5× 103 1.3× 23 0.4× 11 572
Gloria Milani Italy 11 251 0.7× 69 0.5× 57 0.6× 95 1.2× 22 0.4× 16 420
Markus Schick Germany 11 209 0.6× 107 0.8× 39 0.4× 32 0.4× 23 0.4× 24 320
Liliana H. Mochmann Germany 10 278 0.8× 71 0.5× 50 0.5× 155 2.0× 29 0.6× 14 436

Countries citing papers authored by Stephen Tirrell

Since Specialization
Citations

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

Fields of papers citing papers by Stephen Tirrell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stephen Tirrell

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

All Works

16 of 16 papers shown
1.
Zhuang, Junling, Fazal Shirazi, Ramesh Singh, et al.. (2019). Ubiquitin-activating enzyme inhibition induces an unfolded protein response and overcomes drug resistance in myeloma. Blood. 133(14). 1572–1584. 57 indexed citations
2.
Theisen, Matthew, Zhongmin Xiang, Stephen Tirrell, et al.. (2018). Abstract 3844: TAK-659, a SYK kinase inhibitor, demonstrates preclinical antitumor activity in solid tumor models. Cancer Research. 78(13_Supplement). 3844–3844. 1 indexed citations
3.
Koenig, Erik, Craig D. Fisher, Hugues Bernard, et al.. (2016). The beagle dog MicroRNA tissue atlas: identifying translatable biomarkers of organ toxicity. BMC Genomics. 17(1). 649–649. 49 indexed citations
4.
Zhuang, Junling, Hans C. Lee, Isere Kuiatse, et al.. (2016). The Anti-Tumor Effect of the Ubiquitin-Activating Enzyme (UAE) Inhibitor TAK-243 on Pre-Clinical Models of Multiple Myeloma. Blood. 128(22). 3296–3296. 4 indexed citations
5.
He, Helen, Zhongmin Xiang, Lunyin Yu, et al.. (2016). A Sensitive IHC Method for Monitoring Autophagy-Specific Markers in Human Tumor Xenografts. PubMed. 2016. 1–11. 7 indexed citations
6.
Yu, Jie, Jessica J. Huck, Matthew Theisen, et al.. (2016). Anti-tumor activity of TAK-659, a dual inhibitor of SYK and FLT-3 kinases, in AML models.. Journal of Clinical Oncology. 34(15_suppl). e14091–e14091. 6 indexed citations
7.
Shah, Jatin J., Andrzej Jakubowiak, Owen A. O’Connor, et al.. (2015). Phase I Study of the Novel Investigational NEDD8-Activating Enzyme Inhibitor Pevonedistat (MLN4924) in Patients with Relapsed/Refractory Multiple Myeloma or Lymphoma. Clinical Cancer Research. 22(1). 34–43. 162 indexed citations
8.
Smith, David C., Thea Kalebic, Jeffrey R. Infante, et al.. (2015). Phase 1 study of ixazomib, an investigational proteasome inhibitor, in advanced non-hematologic malignancies. Investigational New Drugs. 33(3). 652–663. 35 indexed citations
9.
Chattopadhyay, Nibedita, Allison Berger, Erik Koenig, et al.. (2015). KRAS Genotype Correlates with Proteasome Inhibitor Ixazomib Activity in Preclinical In Vivo Models of Colon and Non-Small Cell Lung Cancer: Potential Role of Tumor Metabolism. PLoS ONE. 10(12). e0144825–e0144825. 14 indexed citations
10.
Shinde, Vaishali, Kristine Burke, Arijit Chakravarty, et al.. (2013). Applications of Pathology-Assisted Image Analysis of Immunohistochemistry-Based Biomarkers in Oncology. Veterinary Pathology. 51(1). 292–303. 16 indexed citations
11.
Bacco, Alessandra Di, Allison Berger, Neeraj Gupta, et al.. (2012). Tumor drug distribution and target engagement of MLN9708, an investigational proteasome inhibitor, in patients with advanced solid tumors.. Journal of Clinical Oncology. 30(15_suppl). 3077–3077. 3 indexed citations
12.
Koenig, Erik, George Mulligan, Eric S. Lightcap, et al.. (2011). Abstract A196: Development, validation, and clinical implementation of a peripheral blood RT-PCR pharmacodynamic assay for MLN4924, an investigational small molecule inhibitor of NEDD8-activating enzyme (NAE).. Molecular Cancer Therapeutics. 10(11_Supplement). A196–A196. 1 indexed citations
13.
McDonald, Alice, Kristine Burke, Michaël Thomas, et al.. (2011). Abstract A38: Development and implementation of immunohistochemistry (IHC)-based pharmacodynamic (PD) biomarkers demonstrate NAE pathway inhibition in MLN4924 solid tumor clinical trials.. Molecular Cancer Therapeutics. 10(11_Supplement). A38–A38. 1 indexed citations
14.
Lu, Shaolei, Karsten Becker, Haoheng Yan, et al.. (2008). Transcriptional Responses to Estrogen and Progesterone in Mammary Gland Identify Networks Regulating p53 Activity. Endocrinology. 149(10). 4809–4820. 24 indexed citations
15.
Anderson, K., Kenneth R. Hess, Mini Kapoor, et al.. (2006). Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments. Clinical Cancer Research. 12(6). 1721–1727. 22 indexed citations
16.
Stec, James, Jing Wang, Kevin R. Coombes, et al.. (2005). Comparison of the Predictive Accuracy of DNA Array-Based Multigene Classifiers across cDNA Arrays and Affymetrix GeneChips. Journal of Molecular Diagnostics. 7(3). 357–367. 41 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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