Pay Gießelmann

521 total citations
5 papers, 214 citations indexed

About

Pay Gießelmann is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, Pay Gießelmann has authored 5 papers receiving a total of 214 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 1 paper in Cellular and Molecular Neuroscience and 1 paper in Artificial Intelligence. Recurrent topics in Pay Gießelmann's work include RNA modifications and cancer (2 papers), Cancer-related gene regulation (2 papers) and RNA Research and Splicing (2 papers). Pay Gießelmann is often cited by papers focused on RNA modifications and cancer (2 papers), Cancer-related gene regulation (2 papers) and RNA Research and Splicing (2 papers). Pay Gießelmann collaborates with scholars based in Germany, United States and China. Pay Gießelmann's co-authors include Alexander Meissner, Frank Müller, Björn Brändl, Helene Kretzmer, Christina Galonska, Elena K. Stamenova, Jing Liao, Ole Ammerpohl, Éric Martin and Günter Assum and has published in prestigious journals such as Nature Genetics, Nature Biotechnology and Bioinformatics.

In The Last Decade

Pay Gießelmann

5 papers receiving 211 citations

Peers

Pay Gießelmann
Comparison fields: 5 of 45
  • Molecular Biology 195
  • Genetics 44
  • Cellular and Molecular Neuroscience 27
  • Plant Science 18
  • Biomedical Engineering 15
Replace Colin P. Florian with:
Colin P. Florian United States
Yingzi Yue United States
Diego Garrido-Martín Spain
Dylan Stavish United Kingdom
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Claudia Chavarria United States
Megan S. Hogan United States
Huanan Ren Canada
Julia Uraji United Kingdom
Jiangwei Lin China
Colin P. Florian United States View profile →
Citations per field, relative to Pay Gießelmann
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Citations per year, relative to Pay Gießelmann
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Countries citing papers authored by Pay Gießelmann

Since Specialization
Citations

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

Fields of papers citing papers by Pay Gießelmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pay Gießelmann

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

All Works

5 of 5 papers shown
# Work Indexed citations
1 2
2 5
3 82
4 117
5 8

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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2026