Mark Gosink

1.7k total citations
33 papers, 1.3k citations indexed

About

Mark Gosink is a scholar working on Molecular Biology, Genetics and Oncology. According to data from OpenAlex, Mark Gosink has authored 33 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 7 papers in Genetics and 6 papers in Oncology. Recurrent topics in Mark Gosink's work include Ubiquitin and proteasome pathways (6 papers), Computational Drug Discovery Methods (5 papers) and Virus-based gene therapy research (3 papers). Mark Gosink is often cited by papers focused on Ubiquitin and proteasome pathways (6 papers), Computational Drug Discovery Methods (5 papers) and Virus-based gene therapy research (3 papers). Mark Gosink collaborates with scholars based in United States, Canada and United Kingdom. Mark Gosink's co-authors include Richard D. Vierstra, Jan Smalle, Dong‐Yul Sung, Tessa Durham Brooks, Joseph Walker, Seth J Davis, Jasmina Kurepa, Janet M. Allen, László Takács and Alirio J. Melendez and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Mark Gosink

31 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Gosink United States 18 927 220 197 162 124 33 1.3k
Jeong Soo Yang South Korea 11 1.8k 2.0× 99 0.5× 272 1.4× 156 1.0× 157 1.3× 21 2.3k
Bastien Cautain Spain 23 570 0.6× 90 0.4× 115 0.6× 77 0.5× 85 0.7× 57 1.4k
Nancy L. Andon United States 13 827 0.9× 243 1.1× 68 0.3× 152 0.9× 101 0.8× 16 1.3k
L. Alex Gaither United States 18 873 0.9× 394 1.8× 180 0.9× 59 0.4× 77 0.6× 22 2.0k
Olga Protchenko United States 21 1.2k 1.3× 235 1.1× 85 0.4× 49 0.3× 285 2.3× 29 1.9k
Shogo Ikeda Japan 22 1.4k 1.5× 158 0.7× 251 1.3× 194 1.2× 238 1.9× 75 1.8k
Gareth Chelvanayagam Australia 19 1.9k 2.1× 106 0.5× 127 0.6× 170 1.0× 52 0.4× 49 2.3k
Mathew Traini Australia 16 814 0.9× 113 0.5× 79 0.4× 54 0.3× 62 0.5× 25 1.3k
Brendan P. Eckelman United States 14 1.3k 1.4× 521 2.4× 317 1.6× 77 0.5× 208 1.7× 24 2.1k
Janet Hager United States 9 790 0.9× 475 2.2× 100 0.5× 160 1.0× 98 0.8× 11 1.2k

Countries citing papers authored by Mark Gosink

Since Specialization
Citations

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

Fields of papers citing papers by Mark Gosink

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Gosink

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Gosink. A scholar is included among the top collaborators of Mark Gosink 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 Mark Gosink. Mark Gosink 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.
Qiu, Luping, Steven W. Kumpf, Elias M. Oziolor, et al.. (2025). In vitro NIH3T3 mouse embryonic fibroblast cell model does not predict AAV2 or AAVdj-mediated cell transformation. Toxicology and Applied Pharmacology. 495. 117229–117229. 2 indexed citations
2.
Oziolor, Elias M., Steven W. Kumpf, Mark Gosink, et al.. (2023). Comparing molecular and computational approaches for detecting viral integration of AAV gene therapy constructs. Molecular Therapy — Methods & Clinical Development. 29. 395–405. 14 indexed citations
3.
Gosink, Mark, et al.. (2022). Evaluating molecular fingerprint-based models of drug side effects against a statistical control. Drug Discovery Today. 27(11). 103364–103364. 6 indexed citations
4.
Krauskopf, Julian, Mark Gosink, Shelli Schomaker, et al.. (2020). The MicroRNA-based Liquid Biopsy Improves Early Assessment of Lethal Acetaminophen Poisoning: A Case Report. American Journal of Case Reports. 21. e919289–e919289. 6 indexed citations
5.
Dallaire, Paul, Karim Nagi, Mark Gosink, et al.. (2019). Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response. Nature Communications. 10(1). 4075–4075. 32 indexed citations
6.
Coskran, Timothy, Zhijie Jiang, James E. Klaunig, et al.. (2017). Induction of endogenous retroelements as a potential mechanism for mouse-specific drug-induced carcinogenicity. PLoS ONE. 12(5). e0176768–e0176768. 2 indexed citations
7.
Krauskopf, Julian, Theo M. de Kok, Shelli Schomaker, et al.. (2017). Serum microRNA signatures as "liquid biopsies" for interrogating hepatotoxic mechanisms and liver pathogenesis in human. PLoS ONE. 12(5). e0177928–e0177928. 41 indexed citations
8.
Kamperschroer, Cris, et al.. (2015). The genomic sequence of lymphocryptovirus from cynomolgus macaque. Virology. 488. 28–36. 12 indexed citations
9.
Nassirpour, Rounak, Sachin Mathur, Mark Gosink, et al.. (2014). Identification of tubular injury microRNA biomarkers in urine: comparison of next-generation sequencing and qPCR-based profiling platforms. BMC Genomics. 15(1). 485–485. 72 indexed citations
10.
Lanz, Thomas A., Edward Guilmette, Mark Gosink, et al.. (2013). Transcriptomic analysis of genetically defined autism candidate genes reveals common mechanisms of action. Molecular Autism. 4(1). 45–45. 43 indexed citations
11.
12.
Griffith, Ann V., Mohammad Fallahi, Hiroshi Nakase, et al.. (2009). Spatial Mapping of Thymic Stromal Microenvironments Reveals Unique Features Influencing T Lymphoid Differentiation. Immunity. 31(6). 999–1009. 66 indexed citations
13.
Laifenfeld, Daphna, Annalyn Gilchrist, David A. Drubin, et al.. (2009). The Role of Hypoxia in 2-Butoxyethanol–Induced Hemangiosarcoma. Toxicological Sciences. 113(1). 254–266. 32 indexed citations
14.
Cervino, Alessandra, Mark Gosink, Mohammad Fallahi, et al.. (2006). A comprehensive mouse IBD database for the efficient localization of quantitative trait loci. Mammalian Genome. 17(6). 565–574. 10 indexed citations
15.
Kurepa, Jasmina, Joseph Walker, Jan Smalle, et al.. (2003). The Small Ubiquitin-like Modifier (SUMO) Protein Modification System in Arabidopsis. Journal of Biological Chemistry. 278(9). 6862–6872. 359 indexed citations
16.
Swaroop, Manju, Mark Gosink, & Yi Sun. (2001). SAG/ROC2/Rbx2/Hrt2 , a Component of SCF E3 Ubiquitin Ligase: Genomic Structure, a Splicing Variant, and Two Family Pseudogenes. DNA and Cell Biology. 20(7). 425–434. 24 indexed citations
17.
Melendez, Alirio J., et al.. (2000). Human sphingosine kinase: molecular cloning, functional characterization and tissue distribution. Gene. 251(1). 19–26. 110 indexed citations
18.
Miller, Paul, et al.. (1999). The identification of a new family of sugar efflux pumps in Escherichia coli. Molecular Microbiology. 31(6). 1845–1851. 42 indexed citations
19.
Gosink, Mark, et al.. (1997). The ubiquitin‐activating enzyme (E1) gene family in Arabidopsis thaliana. The Plant Journal. 11(2). 213–226. 76 indexed citations
20.
Gosink, Mark & Richard D. Vierstra. (1995). Redirecting the specificity of ubiquitination by modifying ubiquitin-conjugating enzymes.. Proceedings of the National Academy of Sciences. 92(20). 9117–9121. 45 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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