Djork-Arné Clevert
- Molecular Biology top 5%
- Computational Theory and Mathematics top 0.2%
- Materials Chemistry top 5%
- Genetics top 10%
- Artificial Intelligence top 10%
- Co-authors
- Sepp HochreiterFloriane MontanariRobin WinterFrank NoéAndreas MayrGünter KlambauerAndreas MittereckerKlaus Obermayer
- Topics
- Computational Drug Discovery Methods (20 papers)Machine Learning in Materials Science (14 papers)Gene expression and cancer classification (10 papers)
- Journals
- Nucleic Acids ResearchNature CommunicationsSHILAP Revista de lepidopterología
- Partner nations
- GermanyAustriaUnited States
In The Last Decade
Djork-Arné Clevert
37 papers receiving 2.4k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Molecular Biology 1.5k
- Computational Theory and Mathematics 1.1k
- Materials Chemistry 765
- Genetics 299
- Artificial Intelligence 197
Countries citing papers authored by Djork-Arné Clevert
This map shows the geographic impact of Djork-Arné Clevert'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 Djork-Arné Clevert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Djork-Arné Clevert more than expected).
Fields of papers citing papers by Djork-Arné Clevert
This network shows the impact of papers produced by Djork-Arné Clevert. 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 Djork-Arné Clevert. The network helps show where Djork-Arné Clevert may publish in the future.
Co-authorship network of co-authors of Djork-Arné Clevert
This figure shows the co-authorship network connecting the top 25 collaborators of Djork-Arné Clevert. A scholar is included among the top collaborators of Djork-Arné Clevert 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 Djork-Arné Clevert. Djork-Arné Clevert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 10 | |
| 5 | 6 | |
| 6 | 16 | |
| 7 | 8 | |
| 8 | 21 | |
| 9 | 12 | |
| 10 | 8 | |
| 11 | 38 | |
| 12 | 40 | |
| 13 | De novo generation of hit-like molecules from gene expression signatures using artificial intelligencebreakdown → | 283 |
| 14 | 156 | |
| 15 | 70 | |
| 16 | 7 | |
| 17 | Learning continuous and data-driven molecular descriptors by translating equivalent chemical representationsbreakdown → | 326 |
| 18 | Rectified factor networks | 2 |
| 19 | 12 | |
| 20 | 40 |
About Djork-Arné Clevert
Djork-Arné Clevert is a scholar working on Computational Theory and Mathematics, Biophysics and Materials Chemistry, having authored 38 papers that have together received 2.4k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (20 papers), Machine Learning in Materials Science (14 papers) and Gene expression and cancer classification (10 papers). The work is most often cited by research in Computational Theory and Mathematics (1.1k citations), Biophysics (132 citations) and Molecular Biology (1.5k citations). Djork-Arné Clevert has collaborated with scholars based in Germany, Austria and United States. Frequent co-authors include Sepp Hochreiter, Floriane Montanari, Robin Winter, Frank Noé, Andreas Mayr, Günter Klambauer, Andreas Mitterecker, Klaus Obermayer, Ulrich Bodenhofer and Joerg Wichard. Their work appears in journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.
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.