Krystyna Keleman

6.0k total citations · 1 hit paper
24 papers, 4.3k citations indexed

About

Krystyna Keleman is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Cell Biology. According to data from OpenAlex, Krystyna Keleman has authored 24 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Cellular and Molecular Neuroscience, 15 papers in Molecular Biology and 8 papers in Cell Biology. Recurrent topics in Krystyna Keleman's work include Neurobiology and Insect Physiology Research (14 papers), Axon Guidance and Neuronal Signaling (7 papers) and Hippo pathway signaling and YAP/TAZ (5 papers). Krystyna Keleman is often cited by papers focused on Neurobiology and Insect Physiology Research (14 papers), Axon Guidance and Neuronal Signaling (7 papers) and Hippo pathway signaling and YAP/TAZ (5 papers). Krystyna Keleman collaborates with scholars based in Austria, United States and Netherlands. Krystyna Keleman's co-authors include Barry J. Dickson, Georg Dietzl, Michaela Fellner, Frank Schnorrer, Kuan-Chung Su, Doris Chen, Africa Couto, Sebastian Krüttner, Mattias Alenius and Carlos Ribeiro and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Krystyna Keleman

23 papers receiving 4.3k citations

Hit Papers

A genome-wide transgenic RNAi library for conditional gen... 2007 2026 2013 2019 2007 500 1000 1.5k 2.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Krystyna Keleman Austria 20 2.6k 2.4k 1.0k 725 509 24 4.3k
Haig Keshishian United States 40 2.4k 0.9× 3.9k 1.6× 1.3k 1.2× 880 1.2× 476 0.9× 73 5.2k
Yoshiki Hotta Japan 35 3.0k 1.1× 2.2k 0.9× 934 0.9× 710 1.0× 376 0.7× 63 4.6k
Mani Ramaswami United States 41 3.3k 1.2× 2.5k 1.0× 1.2k 1.1× 759 1.0× 332 0.7× 90 5.3k
P. Robin Hiesinger United States 33 2.6k 1.0× 2.0k 0.8× 1.7k 1.6× 663 0.9× 337 0.7× 69 4.8k
Georg Dietzl Austria 6 2.2k 0.8× 1.6k 0.7× 979 1.0× 440 0.6× 498 1.0× 6 3.4k
Sean T. Sweeney United Kingdom 29 2.0k 0.8× 2.3k 1.0× 1.3k 1.2× 592 0.8× 315 0.6× 78 4.3k
Stefan Thor Sweden 36 4.0k 1.5× 2.3k 0.9× 1.2k 1.1× 788 1.1× 492 1.0× 82 5.4k
Alberto Ferrús Spain 35 2.4k 0.9× 2.2k 0.9× 572 0.6× 639 0.9× 275 0.5× 83 3.8k
Michael J. Bastiani United States 29 2.1k 0.8× 2.1k 0.9× 746 0.7× 376 0.5× 256 0.5× 43 4.0k
Koen J. T. Venken United States 30 3.2k 1.2× 1.8k 0.7× 906 0.9× 904 1.2× 468 0.9× 50 4.8k

Countries citing papers authored by Krystyna Keleman

Since Specialization
Citations

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

Fields of papers citing papers by Krystyna Keleman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Krystyna Keleman

This figure shows the co-authorship network connecting the top 25 collaborators of Krystyna Keleman. A scholar is included among the top collaborators of Krystyna Keleman 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 Krystyna Keleman. Krystyna Keleman 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.
Graeve, Fabienne De, Martina Hallegger, Ugur Dag, et al.. (2025). Axonal RNA localization is essential for long-term memory. Nature Communications. 16(1). 2560–2560.
2.
Zhao, Xiaoliang, et al.. (2018). Persistent activity in a recurrent circuit underlies courtship memory in Drosophila. eLife. 7. 47 indexed citations
3.
Koemans, Tom S., et al.. (2017). <em>Drosophila</em> Courtship Conditioning As a Measure of Learning and Memory. Journal of Visualized Experiments. 30 indexed citations
4.
Koemans, Tom S., et al.. (2017). <em>Drosophila</em> Courtship Conditioning As a Measure of Learning and Memory. Journal of Visualized Experiments. 2 indexed citations
5.
Stepien, Barbara K., Daniel Gerlach, Ugur Dag, et al.. (2016). RNA-binding profiles of Drosophila CPEB proteins Orb and Orb2. Proceedings of the National Academy of Sciences. 113(45). E7030–E7038. 42 indexed citations
6.
Gai, Yunchao, et al.. (2015). Identification of Genes That Promote or Inhibit Olfactory Memory Formation in Drosophila. Genetics. 199(4). 1173–1182. 65 indexed citations
7.
Krüttner, Sebastian, Lisa Traunmüller, Ugur Dag, et al.. (2015). Synaptic Orb2A Bridges Memory Acquisition and Late Memory Consolidation in Drosophila. Cell Reports. 11(12). 1953–1965. 59 indexed citations
8.
Krüttner, Sebastian, Barbara K. Stepien, Jasprina N. Noordermeer, et al.. (2012). Drosophila CPEB Orb2A Mediates Memory Independent of Its RNA-Binding Domain. Neuron. 76(2). 383–395. 75 indexed citations
9.
Keleman, Krystyna, Eleftheria Vrontou, Sebastian Krüttner, et al.. (2012). Dopamine neurons modulate pheromone responses in Drosophila courtship learning. Nature. 489(7414). 145–149. 160 indexed citations
10.
Kramer, Jamie M., Merel A.W. Oortveld, Hendrik Marks, et al.. (2011). Epigenetic Regulation of Learning and Memory by Drosophila EHMT/G9a. PLoS Biology. 9(1). e1000569–e1000569. 141 indexed citations
11.
Schnorrer, Frank, Cornelia Schönbauer, Christoph C. H. Langer, et al.. (2010). Systematic genetic analysis of muscle morphogenesis and function in Drosophila. Nature. 464(7286). 287–291. 219 indexed citations
12.
Dietzl, Georg, Doris Chen, Frank Schnorrer, et al.. (2007). A genome-wide transgenic RNAi library for conditional gene inactivation in Drosophila. Nature. 448(7150). 151–156. 2075 indexed citations breakdown →
13.
Keleman, Krystyna, Sebastian Krüttner, Mattias Alenius, & Barry J. Dickson. (2007). Function of the Drosophila CPEB protein Orb2 in long-term courtship memory. Nature Neuroscience. 10(12). 1587–1593. 206 indexed citations
14.
Keleman, Krystyna, Carlos Ribeiro, & Barry J. Dickson. (2005). Comm function in commissural axon guidance: cell-autonomous sorting of Robo in vivo. Nature Neuroscience. 8(2). 156–163. 126 indexed citations
15.
Lundström, Annika, Marco Gallio, Camilla Englund, et al.. (2004). Vilse, a conserved Rac/Cdc42 GAP mediating Robo repulsion in tracheal cells and axons. Genes & Development. 18(17). 2161–2171. 101 indexed citations
16.
Dickson, Barry J. & Krystyna Keleman. (2002). Netrins. Current Biology. 12(5). R154–R155. 16 indexed citations
17.
Keleman, Krystyna, David Teis, Karin Paiha, et al.. (2002). Comm Sorts Robo to Control Axon Guidance at the Drosophila Midline. Cell. 110(4). 415–427. 250 indexed citations
18.
Keleman, Krystyna & Barry J. Dickson. (2001). Short- and Long-Range Repulsion by the Drosophila Unc5 Netrin Receptor. Neuron. 32(4). 605–617. 242 indexed citations
19.
Wittwer, Franz, et al.. (2001). Lilliputian: an AF4/FMR2-related protein that controls cell identity and cell growth. Development. 128(5). 791–800. 46 indexed citations
20.
Newsome, Timothy P., Susanne Schmidt, Georg Dietzl, et al.. (2000). Trio Combines with Dock to Regulate Pak Activity during Photoreceptor Axon Pathfinding in Drosophila. Cell. 101(3). 283–294. 264 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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