Katharina Nekolla

1.1k total citations
17 papers, 813 citations indexed

About

Katharina Nekolla is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Immunology. According to data from OpenAlex, Katharina Nekolla has authored 17 papers receiving a total of 813 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 4 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Immunology. Recurrent topics in Katharina Nekolla's work include Radiomics and Machine Learning in Medical Imaging (3 papers), AI in cancer detection (3 papers) and Quantum Dots Synthesis And Properties (2 papers). Katharina Nekolla is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (3 papers), AI in cancer detection (3 papers) and Quantum Dots Synthesis And Properties (2 papers). Katharina Nekolla collaborates with scholars based in Germany, United Kingdom and Slovakia. Katharina Nekolla's co-authors include Markus Rehberg, Fritz Krombach, Stefan Zahler, Angelika M. Vollmar, Sabine Sellner, Oliver Thorn‐Seshold, Wallis Nahaboo, Malgorzata Borowiak, Marie Delattre and Jens Hasserodt and has published in prestigious journals such as Cell, Blood and Biomaterials.

In The Last Decade

Katharina Nekolla

17 papers receiving 806 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Katharina Nekolla Germany 11 294 228 167 163 125 17 813
Ziyue Karen Jiang United States 12 212 0.7× 44 0.2× 219 1.3× 48 0.3× 105 0.8× 37 800
Takahiro Kuchimaru Japan 18 595 2.0× 104 0.5× 54 0.3× 104 0.6× 275 2.2× 58 1.1k
Ana Paula Candiota Spain 17 217 0.7× 110 0.5× 57 0.3× 28 0.2× 137 1.1× 53 769
Brian D. Gray United States 20 521 1.8× 72 0.3× 137 0.8× 46 0.3× 231 1.8× 70 1.2k
Rupsa Datta United States 13 554 1.9× 83 0.4× 79 0.5× 108 0.7× 550 4.4× 23 1.4k
Jason Chiang United States 17 434 1.5× 42 0.2× 112 0.7× 131 0.8× 68 0.5× 74 1.1k
Yuda Chen United States 14 540 1.8× 123 0.5× 29 0.2× 18 0.1× 177 1.4× 25 874
Tomohiro Murakami Japan 17 347 1.2× 50 0.2× 37 0.2× 118 0.7× 40 0.3× 55 882
Tiffany M. Heaster United States 11 358 1.2× 74 0.3× 83 0.5× 36 0.2× 394 3.2× 16 974
Qiuyu Wu China 18 547 1.9× 41 0.2× 67 0.4× 79 0.5× 65 0.5× 53 1.2k

Countries citing papers authored by Katharina Nekolla

Since Specialization
Citations

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

Fields of papers citing papers by Katharina Nekolla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Katharina Nekolla

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

All Works

17 of 17 papers shown
1.
Meier, Armin, Keith E. Steele, Marlon C. Rebelatto, et al.. (2021). Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images. IEEE Transactions on Medical Imaging. 40(9). 2513–2523. 22 indexed citations
2.
Schönmeyer, Ralf, Germain Forestier, Nicolas Brieu, et al.. (2020). Graph-based description of tertiary lymphoid organs at single-cell level. PLoS Computational Biology. 16(2). e1007385–e1007385. 7 indexed citations
3.
Meier, Armin, Katharina Nekolla, Lindsay C. Hewitt, et al.. (2020). Hypothesis‐free deep survival learning applied to the tumour microenvironment in gastric cancer. The Journal of Pathology Clinical Research. 6(4). 273–282. 26 indexed citations
4.
Harder, Nathalie, Ralf Schönmeyer, Katharina Nekolla, et al.. (2019). Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma. Scientific Reports. 9(1). 7449–7449. 43 indexed citations
5.
Nekolla, Katharina, Nicolas Brieu, Moritz Widmaier, et al.. (2019). Prognostic immunoprofiling of muscle invasive bladder cancer (MIBC) patients in a multicentre setting. Annals of Oncology. 30. v49–v50. 2 indexed citations
6.
Nekolla, Katharina, Anna H. Klemm, Judith‐Irina Buchheim, et al.. (2018). Estimating hemodynamic shear stress in murine peripheral collateral arteries by two-photon line scanning. Molecular and Cellular Biochemistry. 453(1-2). 41–51. 10 indexed citations
7.
Brieu, Nicolas, et al.. (2018). Machine Learning: A Data-Driven Approach to Image Analysis. 69–100. 1 indexed citations
8.
Meier, Armin, Katharina Nekolla, Lindsay C. Hewitt, et al.. (2018). End-to-end learning to predict survival in patients with gastric cancer using convolutional neural networks. Annals of Oncology. 29. viii23–viii23. 9 indexed citations
9.
Sellner, Sabine, Samet Kocabey, Tao Zhang, et al.. (2017). Dexamethasone-conjugated DNA nanotubes as anti-inflammatory agents in vivo. Biomaterials. 134. 78–90. 39 indexed citations
10.
Uhl, Bernd, Gabriele Zuchtriegel, Katharina Nekolla, et al.. (2016). Aged neutrophils contribute to the first line of defense in the acute inflammatory response. Blood. 128(19). 2327–2337. 183 indexed citations
11.
Rehberg, Markus, Katharina Nekolla, Sabine Sellner, et al.. (2016). Intercellular Transport of Nanomaterials is Mediated by Membrane Nanotubes In Vivo. Small. 12(14). 1882–1890. 29 indexed citations
12.
Nekolla, Katharina, Sabine Sellner, Karina Mildner, et al.. (2016). Influence of Surface Modifications on the Spatiotemporal Microdistribution of Quantum Dots In Vivo. Small. 12(19). 2641–2651. 11 indexed citations
13.
Nekolla, Katharina, et al.. (2016). New View on Endothelial Cell Migration. Arteriosclerosis Thrombosis and Vascular Biology. 36(12). 2346–2357. 17 indexed citations
14.
Nekolla, Katharina, Sabine Sellner, Karina Mildner, et al.. (2016). Nanoparticles: Influence of Surface Modifications on the Spatiotemporal Microdistribution of Quantum Dots In Vivo (Small 19/2016). Small. 12(19). 2666–2666. 1 indexed citations
15.
Sellner, Sabine, Samet Kocabey, Katharina Nekolla, et al.. (2015). DNA nanotubes as intracellular delivery vehicles in vivo. Biomaterials. 53. 453–463. 98 indexed citations
16.
Todica, Andrei, Sebastian Lehner, Hao Wang, et al.. (2015). Derivation of a respiration trigger signal in small animal list-mode PET based on respiration-induced variations of the ECG signal. Journal of Nuclear Cardiology. 23(1). 73–83. 2 indexed citations
17.
Borowiak, Malgorzata, Wallis Nahaboo, Martin Reynders, et al.. (2015). Photoswitchable Inhibitors of Microtubule Dynamics Optically Control Mitosis and Cell Death. Cell. 162(2). 403–411. 313 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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