Gunnar Rätsch
Impact in
-
- Face and Expression Recognition
- Artificial Intelligence top 0.1%
- Neural Networks and Applications
Papers in
- Aging 5
-
- Machine Learning and Algorithms 15
- Co-authors
- Bernhard SchölkopfMika SirénK. MüllerKoji TsudaSören SonnenburgJason WestonTakashi OnodaChristin Schäfer
- Journals
- Bioinformatics (19 papers)BMC Bioinformatics (14 papers)PLoS Computational Biology (5 papers)Genome Research (5 papers)Nucleic Acids Research (4 papers)
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
Gunnar Rätsch
159 papers receiving 15.3k citations
Hit Papers
Peers
Comparison fields: 5 of 217
- Computer Vision and Pattern Recognition 4.6k
- Artificial Intelligence 4.9k
- Aging 263
- Signal Processing 1.2k
- Media Technology 858
Countries citing papers authored by Gunnar Rätsch
This map shows the geographic impact of Gunnar Rätsch'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 Gunnar Rätsch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gunnar Rätsch more than expected).
Fields of papers citing papers by Gunnar Rätsch
This network shows the impact of papers produced by Gunnar Rätsch. 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 Gunnar Rätsch. The network helps show where Gunnar Rätsch may publish in the future.
Co-authors
The 25 scholars most cited alongside Gunnar Rätsch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 3 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 14 | |
| 4 | 2023 | 56 | |
| 5 | 2022 | 4 | |
| 6 | 2021 | 3 | |
| 7 | 2019 | 30 | |
| 8 | 2018 | 71 | |
| 9 | 2018 | 11 | |
| 10 | 2016 | 40 | |
| 11 | 2016 | 117 | |
| 12 | 2016 | 101 | |
| 13 | 2015 | 30 | |
| 14 | Multitask Learning in Computational Biology | 2011 | 14 |
| 15 | Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana Hit paper breakdown → | 2007 | 526 |
| 16 | 2007 | 10 | |
| 17 | 2007 | 128 | |
| 18 | 2005 | 56 | |
| 19 | Advanced lectures on machine learning : ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003 : revised lectures | 2004 | 31 |
| 20 | Barrier Boosting | 2000 | 25 |
About Gunnar Rätsch
Gunnar Rätsch is a scholar working on Aging, Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition and Cancer Research, having authored 167 papers that have together received 16.1k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (40 papers), RNA and protein synthesis mechanisms (30 papers), RNA modifications and cancer (25 papers), RNA Research and Splicing (21 papers), Gene expression and cancer classification (17 papers), Machine Learning in Bioinformatics (15 papers), Machine Learning and Algorithms (15 papers) and Face and Expression Recognition (15 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (4.6k citations), Artificial Intelligence (4.9k citations), Aging (263 citations), Signal Processing (1.2k citations) and Media Technology (858 citations). Gunnar Rätsch has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Bernhard Schölkopf, Mika Sirén, K. Müller, Koji Tsuda, Sören Sonnenburg, Jason Weston, Takashi Onoda, Christin Schäfer, Klaus‐Robert Müller and Georg Zeller. Their work appears in journals such as Bioinformatics, BMC Bioinformatics, PLoS Computational Biology, Genome Research and Nucleic Acids Research.
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.