Jana Schor

467 total citations
16 papers, 302 citations indexed

About

Jana Schor is a scholar working on Molecular Biology, Computational Theory and Mathematics and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Jana Schor has authored 16 papers receiving a total of 302 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 3 papers in Computational Theory and Mathematics and 2 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Jana Schor's work include Computational Drug Discovery Methods (3 papers), Genomics and Phylogenetic Studies (3 papers) and Effects and risks of endocrine disrupting chemicals (2 papers). Jana Schor is often cited by papers focused on Computational Drug Discovery Methods (3 papers), Genomics and Phylogenetic Studies (3 papers) and Effects and risks of endocrine disrupting chemicals (2 papers). Jana Schor collaborates with scholars based in Germany, United States and Austria. Jana Schor's co-authors include Jörg Hackermüller, Sebastian Canzler, Kristin Schubert, Ulrike Rolle‐Kampczyk, Martin von Bergen�, Wibke Busch, Hervé Seitz, Roland Buesen, Hennicke Kamp and Michael Karl and has published in prestigious journals such as Nature Communications, Bioinformatics and Environmental Health Perspectives.

In The Last Decade

Jana Schor

13 papers receiving 299 citations

Peers

Jana Schor
Jennifer Atkins United Kingdom
Jeffrey C. Sivils United States
Samantha C. Faber United States
J. Shen China
Zdeněk Andrysík United States
Jana Schor
Citations per year, relative to Jana Schor Jana Schor (= 1×) peers Sebastian Canzler

Countries citing papers authored by Jana Schor

Since Specialization
Citations

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

Fields of papers citing papers by Jana Schor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jana Schor

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

All Works

16 of 16 papers shown
1.
Canzler, Sebastian, et al.. (2025). From toxicogenomics data to cumulative assessment groups: a framework for chemical grouping. Archives of Toxicology. 100(1). 173–191.
2.
Hackermüller, Jörg, et al.. (2025). Automated curation of spatial metadata in environmental monitoring data. Ecological Informatics. 86. 103038–103038.
3.
Karkossa, Isabel, Jana Schor, Martin Wabitsch, et al.. (2024). Unveiling the dynamics of acetylation and phosphorylation in SGBS and 3T3-L1 adipogenesis. iScience. 27(6). 109711–109711. 2 indexed citations
4.
5.
Scheibe, Patrick, et al.. (2023). deepFPlearn +: enhancing toxicity prediction across the chemical universe using graph neural networks. Bioinformatics. 39(12). 2 indexed citations
6.
Schor, Jana, et al.. (2022). AI for predicting chemical-effect associations at the chemical universe level— deepFPlearn. Briefings in Bioinformatics. 23(5). 5 indexed citations
7.
Völkner, Manuela, Felix Wagner, Madalena Carido, et al.. (2022). HBEGF-TNF induce a complex outer retinal pathology with photoreceptor cell extrusion in human organoids. Nature Communications. 13(1). 6183–6183. 32 indexed citations
8.
Schubert, Kristin, Isabel Karkossa, Jana Schor, et al.. (2021). A Multi-Omics Analysis of Mucosal-Associated-Invariant T Cells Reveals Key Drivers of Distinct Modes of Activation. Frontiers in Immunology. 12. 616967–616967. 13 indexed citations
9.
Völkner, Manuela, et al.. (2021). Mouse Retinal Organoid Growth and Maintenance in Longer-Term Culture. Frontiers in Cell and Developmental Biology. 9. 645704–645704. 15 indexed citations
10.
Canzler, Sebastian, Jana Schor, Wibke Busch, et al.. (2020). Prospects and challenges of multi-omics data integration in toxicology. Archives of Toxicology. 94(2). 371–388. 177 indexed citations
11.
Wolf, Stephan, et al.. (2020). A probabilistic version of Sankoff’s maximum parsimony algorithm. Journal of Bioinformatics and Computational Biology. 18(1). 2050004–2050004.
12.
Schor, Jana, et al.. (2020). Potential Co-Factors of an Intraoral Contact Allergy—A Cross-Sectional Study. Dentistry Journal. 8(3). 83–83. 8 indexed citations
13.
Specht, Michael, Sven-Holger Puppel, Gero Doose, et al.. (2019). uap: reproducible and robust HTS data analysis. BMC Bioinformatics. 20(1). 664–664. 12 indexed citations
14.
Bauer, Mario, Jörg Hackermüller, Jana Schor, et al.. (2018). Specific induction of the unique GPR15 expression in heterogeneous blood lymphocytes by tobacco smoking. Biomarkers. 24(3). 217–224. 15 indexed citations
15.
Canzler, Sebastian, Peter F. Stadler, & Jana Schor. (2017). The fungal snoRNAome. RNA. 24(3). 342–360. 9 indexed citations
16.
Schor, Jana, et al.. (2012). Evolution and Phylogeny of MicroRNAs — Protocols, Pitfalls, and Problems. Methods in molecular biology. 2257. 211–233. 5 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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