Enrico Glaab
- Molecular Biology top 5%
- Neurology top 5%
- Biomedical Engineering top 10%
- Plant Science top 10%
- Physiology top 10%
- Co-authors
- Natalio KrasnogorReinhard SchneiderAlfonso ValenciaAnaı̈s BaudotJonathan M. GaribaldiPaul WilmesChristian JägerKacy Greenhalgh
- Topics
- Parkinson's Disease Mechanisms and Treatments (23 papers)Bioinformatics and Genomic Networks (20 papers)Gene expression and cancer classification (12 papers)
- Journals
- Proceedings of the National Academy of SciencesJournal of Biological ChemistryNature Communications
- Partner nations
- LuxembourgGermanyUnited Kingdom
In The Last Decade
Enrico Glaab
79 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 150
- Molecular Biology 1.7k
- Neurology 427
- Biomedical Engineering 413
- Plant Science 344
- Physiology 321
Countries citing papers authored by Enrico Glaab
This map shows the geographic impact of Enrico Glaab'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 Enrico Glaab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Enrico Glaab more than expected).
Fields of papers citing papers by Enrico Glaab
This network shows the impact of papers produced by Enrico Glaab. 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 Enrico Glaab. The network helps show where Enrico Glaab may publish in the future.
Co-authorship network of co-authors of Enrico Glaab
This figure shows the co-authorship network connecting the top 25 collaborators of Enrico Glaab. A scholar is included among the top collaborators of Enrico Glaab 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 Enrico Glaab. Enrico Glaab 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 | 0 | |
| 3 | 0 | |
| 4 | 8 | |
| 5 | 0 | |
| 6 | 16 | |
| 7 | 7 | |
| 8 | 2 | |
| 9 | 5 | |
| 10 | 15 | |
| 11 | 4 | |
| 12 | 0 | |
| 13 | 12 | |
| 14 | 66 | |
| 15 | 127 | |
| 16 | 54 | |
| 17 | Learning pathway-based decision rules to classify microarray cancer samples | 5 |
| 18 | 34 | |
| 19 | 39 | |
| 20 | 79 |
About Enrico Glaab
Enrico Glaab is a scholar working on Neurology, Biological Psychiatry and Neurology, having authored 85 papers that have together received 3.0k indexed citations. Recurring topics across this work include Parkinson's Disease Mechanisms and Treatments (23 papers), Bioinformatics and Genomic Networks (20 papers) and Gene expression and cancer classification (12 papers). The work is most often cited by research in Biological Psychiatry (83 citations), Neurology (427 citations) and Molecular Biology (1.7k citations). Enrico Glaab has collaborated with scholars based in Luxembourg, Germany and United Kingdom. Frequent co-authors include Natalio Krasnogor, Reinhard Schneider, Alfonso Valencia, Anaı̈s Baudot, Jonathan M. Garibaldi, Paul Wilmes, Christian Jäger, Kacy Greenhalgh, Mahesh S. Desai and Joëlle V. Fritz. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.
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.