Kathryn Koszka

1.3k total citations · 1 hit paper
8 papers, 979 citations indexed

About

Kathryn Koszka is a scholar working on Molecular Biology, Neurology and Genetics. According to data from OpenAlex, Kathryn Koszka has authored 8 papers receiving a total of 979 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 3 papers in Neurology and 2 papers in Genetics. Recurrent topics in Kathryn Koszka's work include CRISPR and Genetic Engineering (4 papers), Pluripotent Stem Cells Research (3 papers) and Amyotrophic Lateral Sclerosis Research (3 papers). Kathryn Koszka is often cited by papers focused on CRISPR and Genetic Engineering (4 papers), Pluripotent Stem Cells Research (3 papers) and Amyotrophic Lateral Sclerosis Research (3 papers). Kathryn Koszka collaborates with scholars based in United States, Netherlands and Japan. Kathryn Koszka's co-authors include Kevin Eggan, Justin K. Ichida, Ava C. Carter, Kyle M. Loh, Hidenori Akutsu, Dieter Egli, Lee L. Rubin, Joel Blanchard, Francesco Paolo Di Giorgio and Kelvin Lam and has published in prestigious journals such as Nature Neuroscience, Cell stem cell and Human Molecular Genetics.

In The Last Decade

Kathryn Koszka

8 papers receiving 971 citations

Hit Papers

A Small-Molecule Inhibitor of Tgf-β Signaling Replaces So... 2009 2026 2014 2020 2009 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kathryn Koszka United States 8 777 232 165 131 120 8 979
Imbisaat Geti United Kingdom 6 680 0.9× 188 0.8× 126 0.8× 101 0.8× 235 2.0× 7 960
Yaniv Gil Israel 9 308 0.4× 84 0.4× 69 0.4× 59 0.5× 79 0.7× 11 445
In H. Park South Korea 8 348 0.4× 130 0.6× 261 1.6× 32 0.2× 45 0.4× 9 694
Kee-Pyo Kim Germany 20 1.0k 1.3× 50 0.2× 60 0.4× 121 0.9× 69 0.6× 35 1.3k
Zhihua Feng United States 16 647 0.8× 103 0.4× 574 3.5× 59 0.5× 29 0.2× 33 926
Kari Pollock United States 7 434 0.6× 49 0.2× 124 0.8× 191 1.5× 37 0.3× 9 676
Gabriela Bezáková Slovakia 10 669 0.9× 80 0.3× 50 0.3× 44 0.3× 111 0.9× 15 898
Niko Hensel Germany 16 476 0.6× 130 0.6× 401 2.4× 17 0.1× 65 0.5× 28 753
Yaël Gothelf Israel 16 291 0.4× 258 1.1× 318 1.9× 23 0.2× 70 0.6× 27 798
Tadashi Kanouchi Japan 10 360 0.5× 198 0.9× 75 0.5× 24 0.2× 63 0.5× 19 639

Countries citing papers authored by Kathryn Koszka

Since Specialization
Citations

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

Fields of papers citing papers by Kathryn Koszka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kathryn Koszka

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

All Works

8 of 8 papers shown
1.
Peterson, Michael, Helen McLaughlin, Eric Marshall, et al.. (2021). Highly efficient neuronal gene knockout in vivo by CRISPR-Cas9 via neonatal intracerebroventricular injection of AAV in mice. Gene Therapy. 28(10-11). 646–658. 34 indexed citations
2.
Marsh, Galina, Shanqin Xu, Kathryn Koszka, et al.. (2021). Use of CRISPR/Cas9-mediated disruption of CNS cell type genes to profile transduction of AAV by neonatal intracerebroventricular delivery in mice. Gene Therapy. 28(7-8). 456–468. 13 indexed citations
3.
Suzuki, Naoki, et al.. (2016). SLC52A3 , A Brown–Vialetto–van Laere syndrome candidate gene is essential for mouse development, but dispensable for motor neuron differentiation. Human Molecular Genetics. 25(9). 1814–1823. 13 indexed citations
4.
Burberry, Aaron, Naoki Suzuki, Rob Moccia, et al.. (2016). Loss-of-function mutations in the C9ORF72 mouse ortholog cause fatal autoimmune disease. Science Translational Medicine. 8(347). 347ra93–347ra93. 188 indexed citations
5.
Carter, Ava C., Brandi N. Davis‐Dusenbery, Kathryn Koszka, Justin K. Ichida, & Kevin Eggan. (2014). Nanog-Independent Reprogramming to iPSCs with Canonical Factors. Stem Cell Reports. 2(2). 119–126. 34 indexed citations
6.
Koszka, Kathryn, et al.. (2014). Genetic validation of a therapeutic target in a mouse model of ALS. Science Translational Medicine. 6(248). 248ra104–248ra104. 29 indexed citations
7.
Suzuki, Naoki, Asif Maroof, Florian T. Merkle, et al.. (2013). The mouse C9ORF72 ortholog is enriched in neurons known to degenerate in ALS and FTD. Nature Neuroscience. 16(12). 1725–1727. 56 indexed citations
8.
Ichida, Justin K., Joel Blanchard, Kelvin Lam, et al.. (2009). A Small-Molecule Inhibitor of Tgf-β Signaling Replaces Sox2 in Reprogramming by Inducing Nanog. Cell stem cell. 5(5). 491–503. 612 indexed citations breakdown →

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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