Yann Guermeur
- Molecular Biology
- Renewable Energy, Sustainability and the Environment
- Ecology
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- J. Mark CockBernhard GschloesslPatrick GallinariC. GeourjonNicolas SapayGilbert DeléageAndré ElisseeffHélène Paugam‐Moisy
- Topics
- Machine Learning and Algorithms (6 papers)Machine Learning in Bioinformatics (4 papers)Protein Structure and Dynamics (4 papers)
- Partner nations
- FranceGermanyUnited States
In The Last Decade
Yann Guermeur
16 papers receiving 618 citations
Peers
Comparison fields: 5 of 102
- Molecular Biology 369
- Renewable Energy, Sustainability and the Environment 97
- Ecology 86
- Artificial Intelligence 86
- Computer Vision and Pattern Recognition 71
Countries citing papers authored by Yann Guermeur
This map shows the geographic impact of Yann Guermeur'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 Yann Guermeur with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yann Guermeur more than expected).
Fields of papers citing papers by Yann Guermeur
This network shows the impact of papers produced by Yann Guermeur. 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 Yann Guermeur. The network helps show where Yann Guermeur may publish in the future.
Co-authorship network of co-authors of Yann Guermeur
This figure shows the co-authorship network connecting the top 25 collaborators of Yann Guermeur. A scholar is included among the top collaborators of Yann Guermeur 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 Yann Guermeur. Yann Guermeur is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | 0 | |
| 4 | 8 | |
| 5 | 17 | |
| 6 | 7 | |
| 7 | VC Theory of Large Margin Multi-Category Classifiers | 9 |
| 8 | 167 | |
| 9 | 125 | |
| 10 | 4 | |
| 11 | Large Margin Multi-category Discriminant Models and Scale-sensitive Psi-dimensions | 1 |
| 12 | 20 | |
| 13 | 75 | |
| 14 | 4 | |
| 15 | 185 | |
| 16 | 6 | |
| 17 | 6 |
About Yann Guermeur
Yann Guermeur is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Biophysics, having authored 17 papers that have together received 640 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (6 papers), Machine Learning in Bioinformatics (4 papers) and Protein Structure and Dynamics (4 papers). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (97 citations), Molecular Biology (369 citations) and Oceanography (46 citations). Yann Guermeur has collaborated with scholars based in France, Germany and United States. Frequent co-authors include J. Mark Cock, Bernhard Gschloessl, Patrick Gallinari, C. Geourjon, Nicolas Sapay, Gilbert Deléage, André Elisseeff, Hélène Paugam‐Moisy, Dominique Zelus and Walter Blondel. Their work appears in journals such as Bioinformatics, Optics Express and BMC Bioinformatics.
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.