Kay Grobe

3.4k total citations
66 papers, 2.2k citations indexed

About

Kay Grobe is a scholar working on Molecular Biology, Cell Biology and Genetics. According to data from OpenAlex, Kay Grobe has authored 66 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 31 papers in Cell Biology and 15 papers in Genetics. Recurrent topics in Kay Grobe's work include Proteoglycans and glycosaminoglycans research (28 papers), Hedgehog Signaling Pathway Studies (26 papers) and Fibroblast Growth Factor Research (16 papers). Kay Grobe is often cited by papers focused on Proteoglycans and glycosaminoglycans research (28 papers), Hedgehog Signaling Pathway Studies (26 papers) and Fibroblast Growth Factor Research (16 papers). Kay Grobe collaborates with scholars based in Germany, United States and United Kingdom. Kay Grobe's co-authors include Jeffrey D. Esko, Arnd Petersen, Yi Pan, Xin Zhang, Wolf‐Meinhard Becker, Srinivas Reddy Pallerla, Rita Dreier, Jun-ichi Aikawa, Masafumi Tsujimoto and Tabea Dierker and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Kay Grobe

65 papers receiving 2.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kay Grobe Germany 28 1.6k 1.0k 440 224 151 66 2.2k
William C. Lamanna United States 13 1.1k 0.7× 1.1k 1.1× 192 0.4× 124 0.6× 254 1.7× 17 1.9k
Gerdy B. ten Dam Netherlands 23 998 0.6× 982 1.0× 143 0.3× 159 0.7× 170 1.1× 33 1.5k
Tomoya O. Akama Japan 25 1.2k 0.7× 518 0.5× 408 0.9× 143 0.6× 269 1.8× 65 2.0k
Maria Ringvall Sweden 17 880 0.6× 779 0.8× 168 0.4× 250 1.1× 156 1.0× 21 1.6k
Takako Yoshida‐Moriguchi United States 14 1.6k 1.0× 434 0.4× 239 0.5× 158 0.7× 140 0.9× 16 1.9k
Yutaka Sanai Japan 26 2.1k 1.3× 656 0.6× 305 0.7× 106 0.5× 175 1.2× 67 2.6k
Nenad Tomas̆ević United States 19 987 0.6× 574 0.6× 199 0.5× 353 1.6× 44 0.3× 32 2.0k
Hiroko Habuchi Japan 38 2.6k 1.7× 2.6k 2.6× 569 1.3× 290 1.3× 619 4.1× 66 3.7k
Nobuyuki Kurosawa Japan 27 1.9k 1.2× 591 0.6× 197 0.4× 126 0.6× 379 2.5× 76 2.3k
Edward J. Lose United States 11 818 0.5× 713 0.7× 446 1.0× 236 1.1× 31 0.2× 17 1.4k

Countries citing papers authored by Kay Grobe

Since Specialization
Citations

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

Fields of papers citing papers by Kay Grobe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kay Grobe

This figure shows the co-authorship network connecting the top 25 collaborators of Kay Grobe. A scholar is included among the top collaborators of Kay Grobe 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 Kay Grobe. Kay Grobe 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.
Civera, Monica, Stefano Elli, Isabel Pagani, et al.. (2024). Evolution of SARS-CoV-2 spike trimers towards optimized heparan sulfate cross-linking and inter-chain mobility. Scientific Reports. 14(1). 32174–32174. 1 indexed citations
2.
Grobe, Kay, et al.. (2024). Hedgehog on the Move: Glypican-Regulated Transport and Gradient Formation in Drosophila. Cells. 13(5). 418–418. 1 indexed citations
3.
4.
5.
Iorio, Daniele Di, Seraphine V. Wegner, Daniel Hoffmann, et al.. (2023). Hedgehog is relayed through dynamic heparan sulfate interactions to shape its gradient. Nature Communications. 14(1). 758–758. 13 indexed citations
6.
Goretzko, Jonas, et al.. (2021). Conserved cholesterol-related activities of Dispatched 1 drive Sonic hedgehog shedding from the cell membrane. Journal of Cell Science. 135(5). 9 indexed citations
7.
Magalon, Karine, Céline Zimmer, Bilal El Waly, et al.. (2020). Mature oligodendrocytes bordering lesions limit demyelination and favor myelin repair via heparan sulfate production. eLife. 9. 22 indexed citations
8.
Nandadasa, Sumeda, Lauren W. Wang, Anna O’Donnell, et al.. (2019). Secreted metalloproteases ADAMTS9 and ADAMTS20 have a non-canonical role in ciliary vesicle growth during ciliogenesis. Nature Communications. 10(1). 953–953. 44 indexed citations
9.
Jennings, Richard T., Thomas Vogl, Yan Xu, et al.. (2014). Mouse Macrophages Completely Lacking Rho Subfamily GTPases (RhoA, RhoB, and RhoC) Have Severe Lamellipodial Retraction Defects, but Robust Chemotactic Navigation and Altered Motility. Journal of Biological Chemistry. 289(44). 30772–30784. 44 indexed citations
10.
Bandari, Shyam, et al.. (2014). Mutational analysis of the pumpkin (Cucurbita maxima) phloem exudate lectin, PP2 reveals Ser-104 is crucial for carbohydrate binding. Biochemical and Biophysical Research Communications. 450(1). 622–627. 10 indexed citations
11.
Herzog, Christine, et al.. (2011). The amino acid tryptophan prevents the biosynthesis of dermatan sulfate. Molecular BioSystems. 7(10). 2872–2881. 5 indexed citations
12.
Qu, Xiuxia, Christian Carbe, Chenqi Tao, et al.. (2011). Lacrimal Gland Development and Fgf10-Fgfr2b Signaling Are Controlled by 2-O- and 6-O-sulfated Heparan Sulfate. Journal of Biological Chemistry. 286(16). 14435–14444. 65 indexed citations
13.
Qu, Xiuxia, et al.. (2011). Genetic epistasis between heparan sulfate and FGF–Ras signaling controls lens development. Developmental Biology. 355(1). 12–20. 31 indexed citations
14.
Farshi, Pershang, Stefanie Ohlig, Ute Pickhinke, et al.. (2011). Dual Roles of the Cardin-Weintraub Motif in Multimeric Sonic Hedgehog. Journal of Biological Chemistry. 286(26). 23608–23619. 40 indexed citations
15.
Pan, Yi, et al.. (2006). Ndst1 is Required for FGF Signaling in Early Lens Development. Investigative Ophthalmology & Visual Science. 47(13). 1097–1097. 1 indexed citations
16.
Pan, Yi, et al.. (2006). Heparan sulfate biosynthetic gene Ndst1 is required for FGF signaling in early lens development. Development. 133(24). 4933–4944. 86 indexed citations
17.
Grobe, Kay, et al.. (2005). Cerebral hypoplasia and craniofacial defects in mice lacking heparan sulfateNdst1gene function. Development. 132(16). 3777–3786. 157 indexed citations
18.
Ford-Perriss, M., Scott E. Guimond, Ursula Greferath, et al.. (2002). Variant heparan sulfates synthesized in developing mouse brain differentially regulate FGF signaling. Glycobiology. 12(11). 721–727. 57 indexed citations
19.
Grobe, Kay, Johan Ledin, Maria Ringvall, et al.. (2002). Heparan sulfate and development: differential roles of the N-acetylglucosamine N-deacetylase/N-sulfotransferase isozymes. Biochimica et Biophysica Acta (BBA) - General Subjects. 1573(3). 209–215. 124 indexed citations
20.
Petersen, Arnd, Kay Grobe, Buko Lindner, Max Schlaak, & Wolf‐Meinhard Becker. (1997). Comparison of natural and recombinant isoforms of grass pollen allergens. Electrophoresis. 18(5). 819–825. 22 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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