Andreas Mayr
- Molecular Biology top 10%
- Computational Theory and Mathematics top 0.5%
- Materials Chemistry top 10%
- Artificial Intelligence top 5%
- Genetics top 10%
- Co-authors
- Sepp HochreiterGünter KlambauerThomas UnterthinerDjork-Arné ClevertAndreas MittereckerUlrich BodenhoferHugo CeulemansJörg K. Wegner
- Topics
- Computational Drug Discovery Methods (6 papers)Gene expression and cancer classification (6 papers)Machine Learning in Materials Science (4 papers)
- Partner nations
- AustriaBelgiumUnited States
In The Last Decade
Andreas Mayr
16 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 151
- Molecular Biology 1.0k
- Computational Theory and Mathematics 879
- Materials Chemistry 444
- Artificial Intelligence 262
- Genetics 258
Countries citing papers authored by Andreas Mayr
This map shows the geographic impact of Andreas Mayr'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 Andreas Mayr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Mayr more than expected).
Fields of papers citing papers by Andreas Mayr
This network shows the impact of papers produced by Andreas Mayr. 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 Andreas Mayr. The network helps show where Andreas Mayr may publish in the future.
Co-authorship network of co-authors of Andreas Mayr
This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Mayr. A scholar is included among the top collaborators of Andreas Mayr 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 Andreas Mayr. Andreas Mayr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 9 | |
| 3 | 29 | |
| 4 | 28 | |
| 5 | Large-scale comparison of machine learning methods for drug target prediction on ChEMBLbreakdown → | 352 |
| 6 | 30 | |
| 7 | Speeding up Semantic Segmentation for Autonomous Driving | 165 |
| 8 | DeepTox: Toxicity Prediction using Deep Learningbreakdown → | 638 |
| 9 | Rectified factor networks | 2 |
| 10 | 15 | |
| 11 | 38 | |
| 12 | 1 | |
| 13 | 312 | |
| 14 | 19 | |
| 15 | 206 | |
| 16 | 57 | |
| 17 | 15 |
About Andreas Mayr
Andreas Mayr is a scholar working on Computational Theory and Mathematics, Biophysics and Molecular Biology, having authored 17 papers that have together received 1.9k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (6 papers), Gene expression and cancer classification (6 papers) and Machine Learning in Materials Science (4 papers). The work is most often cited by research in Computational Theory and Mathematics (879 citations), Health Informatics (21 citations) and Molecular Biology (1.0k citations). Andreas Mayr has collaborated with scholars based in Austria, Belgium and United States. Frequent co-authors include Sepp Hochreiter, Günter Klambauer, Thomas Unterthiner, Djork-Arné Clevert, Andreas Mitterecker, Ulrich Bodenhofer, Hugo Ceulemans, Jörg K. Wegner, Karin Schwarzbauer and Marvin Steijaert. Their work appears in journals such as Nucleic Acids Research, Bioinformatics 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.