Christian Widmer
Impact in
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and Algorithms
Papers in
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- Machine Learning and Algorithms 4
- Domain Adaptation and Few-Shot Learning 3
- Neural Networks and Applications 2
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- Machine Learning in Bioinformatics 4
- Genomics and Phylogenetic Studies 3
- Genomics and Chromatin Dynamics 2
- Co-authors
- Gunnar Rätsch (15 shared papers)Bernhard Schölkopf (3 shared papers)Gabriele Schweikert (2 shared papers)Sascha Laubinger (2 shared papers)Detlef Weigel (2 shared papers)Georg Zeller (2 shared papers)Stefan R. Henz (2 shared papers)Timo T. Sachsenberg (2 shared papers)
- Journals
- BMC Bioinformatics (2 papers)Bioinformatics (1 paper)Journal of Machine Learning Research (1 paper)Nature Cell Biology (1 paper)Scientific Reports (1 paper)
- Partner nations
- GermanyUnited StatesUnited Kingdom
In The Last Decade
Christian Widmer
23 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 142
- Artificial Intelligence 366
- Computer Vision and Pattern Recognition 195
- Plant Science 310
- Molecular Biology 520
- Cell Biology 80
Countries citing papers authored by Christian Widmer
This map shows the geographic impact of Christian Widmer'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 Christian Widmer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christian Widmer more than expected).
Fields of papers citing papers by Christian Widmer
This network shows the impact of papers produced by Christian Widmer. 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 Christian Widmer. The network helps show where Christian Widmer may publish in the future.
Co-authors
The 25 scholars most cited alongside Christian Widmer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 245 | |
| 2 | Advances in Neural Information Processing Systems 21 (NIPS 2008) | 2008 | 201 |
| 3 | The SHOGUN Machine Learning Toolbox | 2010 | 189 |
| 4 | 2017 | 128 | |
| 5 | An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis | 2008 | 101 |
| 6 | 2008 | 86 | |
| 7 | 2013 | 84 | |
| 8 | 2016 | 56 | |
| 9 | 2010 | 54 | |
| 10 | 2014 | 50 | |
| 11 | 2010 | 45 | |
| 12 | 2014 | 27 | |
| 13 | 2010 | 26 | |
| 14 | Multitask Learning in Computational Biology | 2011 | 20 |
| 15 | 2010 | 18 | |
| 16 | 2012 | 9 | |
| 17 | Tapkee: an efficient dimension reduction library | 2013 | 9 |
| 18 | 2014 | 5 | |
| 19 | 2021 | 4 | |
| 20 | Multitask Multiple Kernel Learning | 2010 | 1 |
About Christian Widmer
Christian Widmer is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Cancer Research and Genetics, having authored 23 papers that have together received 1.4k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (4 papers), Machine Learning and Algorithms (4 papers), Genomics and Phylogenetic Studies (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Genetic Associations and Epidemiology (3 papers), Plant Molecular Biology Research (2 papers), Neural Networks and Applications (2 papers) and Genomics and Chromatin Dynamics (2 papers). The work is most often cited by research in Artificial Intelligence (366 citations), Computer Vision and Pattern Recognition (195 citations), Plant Science (310 citations), Molecular Biology (520 citations) and Cell Biology (80 citations). Christian Widmer has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Gunnar Rätsch, Bernhard Schölkopf, Gabriele Schweikert, Sascha Laubinger, Detlef Weigel, Georg Zeller, Stefan R. Henz, Timo T. Sachsenberg, Yasemin Altün and Nora C. Toussaint. Their work appears in journals such as BMC Bioinformatics, Bioinformatics, Journal of Machine Learning Research, Nature Cell Biology and Scientific Reports.
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.