Kasper Dinkla

2.4k citations
11 papers · 425 · h-index 7

Impact in

Papers in

Kasper Dinkla

11 papers receiving 415 citations

Peers

Kasper Dinkla
Comparison fields: 5 of 74
  • Computer Vision and Pattern Recognition 108
  • Molecular Biology 264
  • Biophysics 18
  • Statistical and Nonlinear Physics 38
  • Signal Processing 27
Replace Rodrigo Santamaría with:
Rodrigo Santamaría Spain
Fritz Lekschas United States
Mathias Otto Germany
Dirk Koschützki Germany
Wim de Leeuw Netherlands
Loretta Auvil United States
Andreas Gerasch Germany
Meghana Kshirsagar United States
Arnau Mir Spain
Sabine Cornelsen Germany
Kasper Dinkla relative to Rodrigo Santamaría Spain Rodrigo Santamaría's profile →
Citations per field
00.5×
Rodrigo Santamaría · 1×
Citations per year

Countries citing papers authored by Kasper Dinkla

Since Specialization
Citations

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

Fields of papers citing papers by Kasper Dinkla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kasper Dinkla, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kasper Dinkla Line = papers co-authored together Kasper Dinkla links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 2018292
2 201245
3 201239
4 202313
5 201412
6 20167
7 20117
8 20185
9 20242
10 20152
11 20121

About Kasper Dinkla

Kasper Dinkla is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Theory and Mathematics and Biophysics, having authored 11 papers that have together received 425 indexed citations. Recurring topics across this work include Data Visualization and Analytics (5 papers), Bioinformatics and Genomic Networks (4 papers), Complex Network Analysis Techniques (2 papers), Cell Image Analysis Techniques (2 papers), Gene expression and cancer classification (2 papers), Gene Regulatory Network Analysis (2 papers), interferon and immune responses (1 paper) and Sphingolipid Metabolism and Signaling (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (108 citations), Molecular Biology (264 citations), Biophysics (18 citations), Statistical and Nonlinear Physics (38 citations) and Signal Processing (27 citations). Kasper Dinkla has collaborated with scholars based in Netherlands, United States and Switzerland. Frequent co-authors include Michel A. Westenberg, Hendrik Strobelt, Hanspeter Pfister, Jacob M. Luber, Soohyun Lee, Nils Gehlenborg, B. Alver, Nezar Abdennur, Peter J. Park and Nikhil Kumar. Their work appears in journals such as Computer Graphics Forum, IEEE Transactions on Visualization and Computer Graphics, Bioinformatics, BMC Bioinformatics and Genome biology.

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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