Josef A. Käs

16.4k total citations · 3 hit papers
219 papers, 12.2k citations indexed

About

Josef A. Käs is a scholar working on Cell Biology, Biomedical Engineering and Molecular Biology. According to data from OpenAlex, Josef A. Käs has authored 219 papers receiving a total of 12.2k indexed citations (citations by other indexed papers that have themselves been cited), including 111 papers in Cell Biology, 73 papers in Biomedical Engineering and 67 papers in Molecular Biology. Recurrent topics in Josef A. Käs's work include Cellular Mechanics and Interactions (107 papers), 3D Printing in Biomedical Research (43 papers) and Force Microscopy Techniques and Applications (30 papers). Josef A. Käs is often cited by papers focused on Cellular Mechanics and Interactions (107 papers), 3D Printing in Biomedical Research (43 papers) and Force Microscopy Techniques and Applications (30 papers). Josef A. Käs collaborates with scholars based in Germany, United States and India. Josef A. Käs's co-authors include Paul A. Janmey, E. Sackmann, F. C. MacKintosh, Jochen Guck, Revathi Ananthakrishnan, Chih‐Kang Shih, C. Casey Cunningham, Rachel Mahaffy, Anatol W. Fritsch and Tess J. Moon and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

Josef A. Käs

217 papers receiving 11.9k citations

Hit Papers

Optical Deformability as an Inherent Cell Marker for Test... 1995 2026 2005 2015 2005 1995 2001 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Josef A. Käs Germany 52 5.9k 4.6k 3.0k 2.8k 879 219 12.2k
Ben Fabry Germany 69 8.1k 1.4× 5.9k 1.3× 2.4k 0.8× 2.4k 0.8× 521 0.6× 186 14.3k
Margaret L. Gardel United States 52 6.9k 1.2× 3.0k 0.7× 2.4k 0.8× 1.8k 0.6× 581 0.7× 115 10.4k
Jochen Guck Germany 58 5.5k 0.9× 6.4k 1.4× 3.3k 1.1× 2.5k 0.9× 1.3k 1.4× 208 13.8k
Paul Matsudaira United States 59 5.3k 0.9× 3.1k 0.7× 7.9k 2.7× 1.4k 0.5× 678 0.8× 285 18.2k
Xavier Trepat Spain 65 11.1k 1.9× 6.7k 1.4× 4.2k 1.4× 1.8k 0.6× 470 0.5× 136 16.1k
Alexander D. Bershadsky Israel 61 13.0k 2.2× 5.0k 1.1× 5.9k 2.0× 2.5k 0.9× 660 0.8× 135 18.0k
Christoph F. Schmidt Germany 52 4.1k 0.7× 3.3k 0.7× 4.1k 1.4× 3.7k 1.3× 648 0.7× 135 11.8k
Ken Jacobson United States 50 5.1k 0.9× 2.9k 0.6× 8.6k 2.9× 2.4k 0.8× 1.6k 1.9× 112 14.2k
Manfred Radmacher Germany 45 4.2k 0.7× 3.3k 0.7× 2.2k 0.7× 5.9k 2.1× 326 0.4× 97 10.2k
Guillaume Charras United Kingdom 54 7.6k 1.3× 3.4k 0.7× 3.9k 1.3× 1.4k 0.5× 823 0.9× 118 12.1k

Countries citing papers authored by Josef A. Käs

Since Specialization
Citations

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

Fields of papers citing papers by Josef A. Käs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Josef A. Käs. 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 Josef A. Käs. The network helps show where Josef A. Käs may publish in the future.

Co-authorship network of co-authors of Josef A. Käs

This figure shows the co-authorship network connecting the top 25 collaborators of Josef A. Käs. A scholar is included among the top collaborators of Josef A. Käs 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 Josef A. Käs. Josef A. Käs 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.
Kader, Avan, Lisa C. Adams, Dilyana B. Mangarova, et al.. (2024). Sensitivity of magnetic resonance elastography to extracellular matrix and cell motility in human prostate cancer cell line-derived xenograft models. Biomaterials Advances. 161. 213884–213884. 1 indexed citations
2.
Sauer, Frank, Steffen Grosser, Jürgen Lippoldt, et al.. (2024). Effect of non-linear strain stiffening in eDAH and unjamming. Soft Matter. 20(9). 1996–2007. 3 indexed citations
4.
Grosser, Steffen, Frank Sauer, M. Merkel, et al.. (2024). Different contractility modes control cell escape from multicellular spheroids and tumor explants. APL Bioengineering. 8(2). 26110–26110. 2 indexed citations
5.
Wolf, Benjamin, et al.. (2023). Anatomy of the fetal membranes: insights from spinning disk confocal microscopy. Archives of Gynecology and Obstetrics. 309(5). 1919–1923. 2 indexed citations
6.
Gläser, Martin, et al.. (2023). Systematic altering of semiflexible DNA-based polymer networks via tunable crosslinking. Nanoscale. 15(16). 7374–7383. 4 indexed citations
7.
Nowicki, Marcin, Carmen Infante‐Duarte, Stefan Koch, et al.. (2022). Mechanical properties of murine hippocampal subregions investigated by atomic force microscopy and in vivo magnetic resonance elastography. Scientific Reports. 12(1). 16723–16723. 12 indexed citations
8.
Grosser, Steffen, Roland Stange, Frank Sauer, et al.. (2021). Differences in cortical contractile properties between healthy epithelial and cancerous mesenchymal breast cells. New Journal of Physics. 23(10). 103020–103020. 9 indexed citations
9.
Grosser, Steffen, Jürgen Lippoldt, Matthias Merkel, et al.. (2021). Cell and Nucleus Shape as an Indicator of Tissue Fluidity in Carcinoma. Physical Review X. 11(1). 93 indexed citations
10.
Wolf, Benjamin, Lars‐Christian Horn, Bahriye Aktas, et al.. (2019). Roadmap to Local Tumour Growth: Insights from Cervical Cancer. Scientific Reports. 9(1). 12768–12768. 6 indexed citations
11.
Streitberger, Kaspar‐Josche, Felix Schrank, Jürgen Braun, et al.. (2019). How tissue fluidity influences brain tumor progression. Proceedings of the National Academy of Sciences. 117(1). 128–134. 126 indexed citations
12.
Shahryari, Mehrgan, Heiko Tzschätzsch, Jing Guo, et al.. (2019). Tomoelastography Distinguishes Noninvasively between Benign and Malignant Liver Lesions. Cancer Research. 79(22). 5704–5710. 74 indexed citations
13.
Sauer, Frank, Angela Ariza de Schellenberger, Heiko Tzschätzsch, et al.. (2019). Collagen networks determine viscoelastic properties of connective tissues yet do not hinder diffusion of the aqueous solvent. Soft Matter. 15(14). 3055–3064. 53 indexed citations
14.
Schnauß, Jörg, et al.. (2018). Synthetic Transient Crosslinks Program the Mechanics of Soft, Biopolymer‐Based Materials. Advanced Materials. 30(13). e1706092–e1706092. 29 indexed citations
15.
Gläser, Martin, et al.. (2016). Self-assembly of hierarchically ordered structures in DNA nanotube systems. New Journal of Physics. 18(5). 55001–55001. 24 indexed citations
16.
Seltmann, Kristin, Anatol W. Fritsch, Josef A. Käs, & Thomas M. Magin. (2013). Keratins significantly contribute to cell stiffness and impact invasive behavior. Proceedings of the National Academy of Sciences. 110(46). 18507–18512. 200 indexed citations
17.
Cadenas, Cristina, et al.. (2012). ERBB2 overexpression triggers transient high mechanoactivity of breast tumor cells. Cytoskeleton. 69(5). 267–277. 9 indexed citations
18.
Käs, Josef A., et al.. (2010). Origin and Spatial Distribution of Forces in Motile Cells. Bulletin of the American Physical Society. 2010. 1 indexed citations
19.
Forstner, Martin B., et al.. (2000). Single Lipid Diffusion in Langmuir Monolayers. APS. 1 indexed citations
20.
Kopečný, J, et al.. (1998). Cellulolytic enzymes of rumen anaerobic fungi Orpinomyces joyonii and Caecomyces communis. Research in Microbiology. 149(6). 417–427. 25 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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