Daniel J. Zabransky

2.6k total citations
29 papers, 802 citations indexed

About

Daniel J. Zabransky is a scholar working on Molecular Biology, Oncology and Genetics. According to data from OpenAlex, Daniel J. Zabransky has authored 29 papers receiving a total of 802 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 13 papers in Oncology and 7 papers in Genetics. Recurrent topics in Daniel J. Zabransky's work include Cancer Genomics and Diagnostics (6 papers), Pancreatic and Hepatic Oncology Research (5 papers) and BRCA gene mutations in cancer (3 papers). Daniel J. Zabransky is often cited by papers focused on Cancer Genomics and Diagnostics (6 papers), Pancreatic and Hepatic Oncology Research (5 papers) and BRCA gene mutations in cancer (3 papers). Daniel J. Zabransky collaborates with scholars based in United States, Germany and Russia. Daniel J. Zabransky's co-authors include Sriram Subramaniam, Adam E. Bennett, Jeffrey D. Lifson, Alberto Bartesaghi, Rachid Sougrat, Julian W. Bess, Ben Ho Park, Elizabeth M. Jaffee, Rory L. Cochran and Charles G. Drake and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and PLoS ONE.

In The Last Decade

Daniel J. Zabransky

28 papers receiving 792 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel J. Zabransky United States 14 254 204 190 173 108 29 802
Eun Joo Seo South Korea 13 271 1.1× 167 0.8× 89 0.5× 151 0.9× 109 1.0× 30 721
Andrea L. Rose United States 7 156 0.6× 264 1.3× 223 1.2× 115 0.7× 200 1.9× 7 1.1k
Gary L. Buchschacher United States 17 588 2.3× 186 0.9× 348 1.8× 95 0.5× 342 3.2× 42 1.2k
Mario Chamorro United States 12 1.2k 4.9× 161 0.8× 411 2.2× 131 0.8× 61 0.6× 13 1.6k
Zhenyu Zhou China 19 872 3.4× 117 0.6× 239 1.3× 596 3.4× 25 0.2× 56 1.5k
Jennifer L. McCann United States 15 722 2.8× 159 0.8× 185 1.0× 144 0.8× 227 2.1× 23 1.0k
Lela Lackey United States 14 1.1k 4.4× 250 1.2× 282 1.5× 357 2.1× 452 4.2× 21 1.6k
M.H.F. Sullivan United Kingdom 22 598 2.4× 164 0.8× 155 0.8× 67 0.4× 27 0.3× 42 1.5k
Peter Brader Austria 21 227 0.9× 67 0.3× 275 1.4× 115 0.7× 14 0.1× 46 1.2k

Countries citing papers authored by Daniel J. Zabransky

Since Specialization
Citations

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

Fields of papers citing papers by Daniel J. Zabransky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel J. Zabransky. 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 Daniel J. Zabransky. The network helps show where Daniel J. Zabransky may publish in the future.

Co-authorship network of co-authors of Daniel J. Zabransky

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel J. Zabransky. A scholar is included among the top collaborators of Daniel J. Zabransky 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 Daniel J. Zabransky. Daniel J. Zabransky 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.
Nakazawa, Mari, Waqar Arif, Ezra Baraban, et al.. (2025). Profiling ADC targets in cholangiocarcinoma: implications for therapeutic development. npj Precision Oncology. 9(1). 364–364.
2.
3.
Zabransky, Daniel J., Yash Chhabra, Mitchell E. Fane, et al.. (2024). Fibroblasts in the Aged Pancreas Drive Pancreatic Cancer Progression. Cancer Research. 84(8). 1221–1236. 13 indexed citations
4.
Hüser, Laura, Yash Chhabra, Олеся Гололобова, et al.. (2024). Aged fibroblast-derived extracellular vesicles promote angiogenesis in melanoma. Cell Reports. 43(9). 114721–114721. 6 indexed citations
5.
Patel, Jignasha, Daniel J. Zabransky, Elana J. Fertig, et al.. (2024). Abstract 1577: Cancer associated fibroblast - tumor cell crosstalk enhances epithelial to mesenchymal transition and promotes a classical to basal switch in human pancreatic ductal adenocarcinoma. Cancer Research. 84(6_Supplement). 1577–1577. 1 indexed citations
6.
Chhabra, Yash, et al.. (2023). Abstract 71: Melanoma alters the bone marrow immune composition in an age-related manner. Cancer Research. 83(7_Supplement). 71–71. 1 indexed citations
7.
Zabransky, Daniel J., Elizabeth M. Jaffee, & Ashani T. Weeraratna. (2022). Shared genetic and epigenetic changes link aging and cancer. Trends in Cell Biology. 32(4). 338–350. 35 indexed citations
8.
Beierl, Katie, Christopher D. Gocke, Daniel J. Zabransky, et al.. (2017). Whole-Exome Sequencing of Metaplastic Breast Carcinoma Indicates Monoclonality with Associated Ductal Carcinoma Component. Clinical Cancer Research. 23(16). 4875–4884. 29 indexed citations
9.
Croessmann, Sarah, Hong Yuen Wong, Daniel J. Zabransky, et al.. (2017). PIK3CA mutations and TP53 alterations cooperate to increase cancerous phenotypes and tumor heterogeneity. Breast Cancer Research and Treatment. 162(3). 451–464. 17 indexed citations
10.
Cochran, Rory L., Justin Cidado, Daniel J. Zabransky, et al.. (2015). Functional isogenic modeling of BRCA1 alleles reveals distinct carrier phenotypes. Oncotarget. 6(28). 25240–25251. 9 indexed citations
11.
Toro, Patricia Valda, Bracha Erlanger, Julia A. Beaver, et al.. (2015). Comparison of cell stabilizing blood collection tubes for circulating plasma tumor DNA. Clinical Biochemistry. 48(15). 993–998. 83 indexed citations
12.
Wong, Hong Yuen, Grace M. Wang, Sarah Croessmann, et al.. (2015). TMSB4Yis a candidate tumor suppressor on the Y chromosome and is deleted in male breast cancer. Oncotarget. 6(42). 44927–44940. 29 indexed citations
13.
Cochran, Rory L., Karen Cravero, David Chu, et al.. (2014). Analysis of BRCA2 loss of heterozygosity in tumor tissue using droplet digital polymerase chain reaction. Human Pathology. 45(7). 1546–1550. 10 indexed citations
14.
Forde, Patrick M., Rory L. Cochran, Sosipatros A. Boikos, et al.. (2014). Familial GI Stromal Tumor With Loss of Heterozygosity and Amplification of Mutant KIT. Journal of Clinical Oncology. 34(3). e13–e16. 9 indexed citations
15.
16.
Zabransky, Daniel J., Christopher J. Nirschl, Nicholas M. Durham, et al.. (2012). Phenotypic and Functional Properties of Helios+ Regulatory T Cells. PLoS ONE. 7(3). e34547–e34547. 122 indexed citations
17.
Zabransky, Daniel J., Heath A. Smith, Christopher J. Thoburn, et al.. (2011). Lenalidomide modulates IL‐8 and anti‐prostate antibody levels in men with biochemically recurrent prostate cancer. The Prostate. 72(5). 487–498. 13 indexed citations
18.
Hallinger, Kelly K., et al.. (2010). Birdsong Differs between Mercury-polluted and Reference Sites. The Auk. 127(1). 156–161. 56 indexed citations
19.
Aganj, Iman, Adam E. Bennett, Mario J. Borgnia, et al.. (2008). Evaluation of denoising algorithms for biological electron tomography. Journal of Structural Biology. 164(1). 7–17. 28 indexed citations
20.
Sougrat, Rachid, Alberto Bartesaghi, Jeffrey D. Lifson, et al.. (2007). Electron Tomography of the Contact between T Cells and SIV/HIV-1: Implications for Viral Entry. PLoS Pathogens. 3(5). e63–e63. 159 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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