Robert Küffner

3.9k citations
31 papers · 2.4k indexed · 1 hit paper · h-index 18

Robert Küffner

31 papers receiving 2.3k citations

Hit Papers

Wisdom of crowds for robust gene network inference1.1k20122026201620212505007501000

Peers

Robert Küffner
Comparison fields: 5 of 146
  • Molecular Biology 2.0k
  • Artificial Intelligence 445
  • Computational Theory and Mathematics 176
  • Cancer Research 149
  • Biophysics 50
Replace William Noble Grundy with:
William Noble Grundy United States
Stefan Bleuler Switzerland
Phillip Lord United Kingdom
Xuewen Chen United States
Jianhua Xuan United States
Christian Stolte United States
Corrado Priami Italy
Marc Fiume Canada
Sebastian Bauer Germany
Robert Küffner relative to William Noble Grundy United States William Noble Grundy's profile →
Citations per field
00.5×8.2×
William Noble Grundy · 1×
Citations per year

Countries citing papers authored by Robert Küffner

Since Specialization
Citations

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

Fields of papers citing papers by Robert Küffner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Robert Küffner, 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 Robert Küffner Line = papers co-authored together Robert Küffner links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20185
2 201417
3 201420
4 201347
5 201315
6 201222
7 201214
8
Wisdom of crowds for robust gene network inferencebreakdown →
20121110
9 20125
10 201124
11 201150
12 201031
13 201065
14 200743
15 200721
16
Characterization of protein interactions
20063
17 200671
18
Data processing effects on the interpretation of microarray gene expression experiments
20054
19 200087
20 199883

About Robert Küffner

Robert Küffner is a scholar working on Molecular Biology, Geochemistry and Petrology and Computational Theory and Mathematics, having authored 31 papers that have together received 2.4k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (17 papers), Gene Regulatory Network Analysis (10 papers), Gene expression and cancer classification (9 papers), Microbial Metabolic Engineering and Bioproduction (4 papers), Biomedical Text Mining and Ontologies (3 papers), Molecular Biology Techniques and Applications (3 papers), RNA Research and Splicing (3 papers) and Genomics and Chromatin Dynamics (3 papers). The work is most often cited by research in Molecular Biology (2.0k citations), Artificial Intelligence (445 citations) and Computational Theory and Mathematics (176 citations). Robert Küffner has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Ralf Zimmer, Katrin Fundel, Nic M. Vega, Kyle R. Allison, Robert J. Prill, Diogo M. Camacho, James C. Costello, Daniel Marbach, James J. Collins and Manolis Kellis. Their work appears in journals such as Bioinformatics, PLoS ONE, Systematic and Applied Microbiology, Nucleic Acids Research and Nature Methods.

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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