Rupert Ecker

3.4k total citations
35 papers, 2.0k citations indexed

About

Rupert Ecker is a scholar working on Artificial Intelligence, Biophysics and Molecular Biology. According to data from OpenAlex, Rupert Ecker has authored 35 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 11 papers in Biophysics and 10 papers in Molecular Biology. Recurrent topics in Rupert Ecker's work include AI in cancer detection (11 papers), Cell Image Analysis Techniques (10 papers) and Single-cell and spatial transcriptomics (4 papers). Rupert Ecker is often cited by papers focused on AI in cancer detection (11 papers), Cell Image Analysis Techniques (10 papers) and Single-cell and spatial transcriptomics (4 papers). Rupert Ecker collaborates with scholars based in Austria, United Kingdom and Australia. Rupert Ecker's co-authors include Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Isabella Ellinger, Georg Steiner, Georg Dorffner, Georg Stingl, Adelheid Elbe‐Bürger, Gero Kramer and Michael Marberger and has published in prestigious journals such as Blood, PLoS ONE and Biochemical Journal.

In The Last Decade

Rupert Ecker

34 papers receiving 2.0k citations

Peers

Rupert Ecker
Comparison fields: 5 of 131
  • Oncology 793
  • Artificial Intelligence 562
  • Molecular Biology 364
  • Immunology 321
  • Epidemiology 269
Replace Fernando U. Garcia with:
Fernando U. Garcia United States
Sharon Nofech‐Mozes Canada
Håvard E. Danielsen Norway
Cindy Franklin Germany
Niels Grabe Germany
Michael Khan United Kingdom
Peter Bult Netherlands
Shumpei Ishikawa Japan
Panu E. Kovanen Finland
Marylène Lejeune Spain
Fernando U. Garcia United States View profile →
Citations per field, relative to Rupert Ecker
Rupert Ecker · 1×
Citations per year, relative to Rupert Ecker
Rupert Ecker · 1×

Countries citing papers authored by Rupert Ecker

Since Specialization
Citations

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

Fields of papers citing papers by Rupert Ecker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rupert Ecker

This figure shows the co-authorship network connecting the top 25 collaborators of Rupert Ecker. A scholar is included among the top collaborators of Rupert Ecker 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 Rupert Ecker. Rupert Ecker 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
# Work Indexed citations
1 14
2 17
3 6
4 70
5 11
6 210
7 8
8 185
9 11
10 272
11 24
12 36
13 24
14 6
15 50
16 80
17 91
18 62
19 156
20 66

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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