Stefan Krämer

4.4k total citations
70 papers, 2.3k citations indexed

About

Stefan Krämer is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Stefan Krämer has authored 70 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 27 papers in Computational Theory and Mathematics and 12 papers in Artificial Intelligence. Recurrent topics in Stefan Krämer's work include Computational Drug Discovery Methods (23 papers), Renal Transplantation Outcomes and Treatments (11 papers) and Metabolomics and Mass Spectrometry Studies (10 papers). Stefan Krämer is often cited by papers focused on Computational Drug Discovery Methods (23 papers), Renal Transplantation Outcomes and Treatments (11 papers) and Metabolomics and Mass Spectrometry Studies (10 papers). Stefan Krämer collaborates with scholars based in Germany, Austria and Switzerland. Stefan Krämer's co-authors include Christoph Helma, Luc De Raedt, Klemens Budde, Wolfgang Arns, Claudia Sommerer, Harald Gschaidmeier, Tobias Cramer, Markus F. Templin, Jochen M. Schwenk and Dieter Stoll and has published in prestigious journals such as New England Journal of Medicine, The Lancet and Bioinformatics.

In The Last Decade

Stefan Krämer

69 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
Stefan Krämer Germany 22 1.0k 413 405 374 343 70 2.3k
Ina Koch Germany 26 2.1k 2.1× 325 0.8× 133 0.3× 181 0.5× 160 0.5× 107 3.3k
Marco Masseroli Italy 23 1.0k 1.0× 110 0.3× 125 0.3× 310 0.8× 49 0.1× 135 2.0k
Jason Flannick United States 24 959 0.9× 534 1.3× 784 1.9× 459 1.2× 9 0.0× 48 2.7k
S. Joshua Swamidass United States 33 1.9k 1.9× 1.7k 4.2× 110 0.3× 382 1.0× 22 0.1× 87 3.4k
Aris Floratos United States 15 1.1k 1.1× 185 0.4× 474 1.2× 222 0.6× 10 0.0× 26 2.5k
Xiaowei Yan United States 31 1.3k 1.3× 134 0.3× 188 0.5× 182 0.5× 14 0.0× 119 3.5k
Qingyu Zhou United States 26 797 0.8× 53 0.1× 47 0.1× 672 1.8× 94 0.3× 91 2.6k
Felix Agakov United Kingdom 21 549 0.5× 77 0.2× 391 1.0× 463 1.2× 5 0.0× 44 2.3k
Peter Shaw United States 27 1.3k 1.3× 148 0.4× 258 0.6× 176 0.5× 13 0.0× 117 3.3k
Jinjin Guo China 28 1.6k 1.5× 57 0.1× 202 0.5× 190 0.5× 17 0.0× 118 2.8k

Countries citing papers authored by Stefan Krämer

Since Specialization
Citations

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

Fields of papers citing papers by Stefan Krämer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stefan Krämer

This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Krämer. A scholar is included among the top collaborators of Stefan Krämer 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 Stefan Krämer. Stefan Krämer 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.
2.
Krämer, Stefan, Andreas Moritz, Meike Hutt, et al.. (2023). An ultra-high-throughput screen for the evaluation of peptide HLA-Binder interactions. Scientific Reports. 13(1). 5290–5290. 4 indexed citations
3.
Krämer, Stefan, et al.. (2023). A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction. BMC Bioinformatics. 24(1). 45–45. 3 indexed citations
4.
Krämer, Stefan, et al.. (2020). Digital DNA microarray generation on glass substrates. Scientific Reports. 10(1). 5770–5770. 20 indexed citations
5.
Krämer, Stefan, et al.. (2019). How to copy and paste DNA microarrays. Scientific Reports. 9(1). 13940–13940. 4 indexed citations
6.
Rolfs, Frank, Marcel Huber, Andreas Kuehne, et al.. (2015). Nrf2 Activation Promotes Keratinocyte Survival during Early Skin Carcinogenesis via Metabolic Alterations. Cancer Research. 75(22). 4817–4829. 42 indexed citations
7.
Krämer, Stefan, et al.. (2013). Improving structural similarity based virtual screening using background knowledge. Journal of Cheminformatics. 5(1). 50–50. 1 indexed citations
8.
Krämer, Stefan, et al.. (2012). Predicting the lung compliance of mechanically ventilated patients via statistical modeling. Physiological Measurement. 33(3). 345–359. 3 indexed citations
9.
Budde, Klemens, Thomas Becker, Wolfgang Arns, et al.. (2011). Everolimus-based, calcineurin-inhibitor-free regimen in recipients of de-novo kidney transplants: an open-label, randomised, controlled trial. The Lancet. 377(9768). 837–847. 272 indexed citations
10.
Richter, Lothar, et al.. (2011). Predicting a small molecule-kinase interaction map: A machine learning approach. Journal of Cheminformatics. 3(1). 22–22. 14 indexed citations
11.
Sommerer, Claudia, Petra Glander, Wolfgang Arns, et al.. (2011). Safety and Efficacy of Intensified Versus Standard Dosing Regimens of Enteric-Coated Mycophenolate Sodium in De Novo Renal Transplant Patients. Transplantation. 91(7). 779–785. 22 indexed citations
12.
Richter, Lothar, et al.. (2010). Integrating background knowledge from internet databases into predictive toxicology models. SAR and QSAR in environmental research. 21(1-2). 21–35. 2 indexed citations
13.
Walz, Gerd, Klemens Budde, Marwan Mannaa, et al.. (2010). Everolimus in Patients with Autosomal Dominant Polycystic Kidney Disease. New England Journal of Medicine. 363(9). 830–840. 433 indexed citations
14.
Shtatland, Timur, et al.. (2009). Enhancing navigation in biomedical databases by community voting and database-driven text classification. BMC Bioinformatics. 10(1). 317–317. 24 indexed citations
15.
Lupescu, Adrian, Corinna Geiger, Naima Zahir, et al.. (2009). Inhibition of Na<sup>+</sup>/H<sup>+</sup> Exchanger Activity by Parvovirus B19 Protein NS1. Cellular Physiology and Biochemistry. 23(1-3). 211–220. 28 indexed citations
16.
Toivonen, Hannu, A. Srinivasan, Ross D. King, Stefan Krämer, & Christoph Helma. (2003). Statistical evaluation of the Predictive ToxicologyChallenge 2000–2001. Bioinformatics. 19(10). 1183–1193. 9 indexed citations
17.
Raedt, Luc De & Stefan Krämer. (2001). The levelwise version space algorithm and its application to molecular fragment finding. Lirias. 853–859. 78 indexed citations
18.
Helma, Christoph, Stefan Krämer, Bernhard Pfahringer, & Eva Gottmann. (2000). Data quality in predictive toxicology: identification of chemical structures and calculation of chemical properties.. Environmental Health Perspectives. 108(11). 1029–1033. 14 indexed citations
19.
Krämer, Stefan. (1996). Structural regression trees. National Conference on Artificial Intelligence. 812–819. 39 indexed citations
20.
Krämer, Stefan, et al.. (1969). [Problems and diagnosis of cerebellar symptomatology in early childhood].. PubMed. 105(1). 80–7. 2 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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