Erfan Younesi
- Molecular Biology
- Artificial Intelligence top 10%
- Physiology
- Computational Theory and Mathematics top 5%
- Psychiatry and Mental health
- Co-authors
- Martin Hofmann‐ApitiusB. G. MüllerAlpha Tom KodamullilLeila ShahmoradiNatalia NovacMichael T. HenekaHarald HampelStanley Durrleman
- Topics
- Bioinformatics and Genomic Networks (18 papers)Biomedical Text Mining and Ontologies (15 papers)Computational Drug Discovery Methods (13 papers)
- Journals
- SHILAP Revista de lepidopterologíaBioinformaticsScientific Reports
- Partner nations
- GermanyUnited KingdomIran
In The Last Decade
Erfan Younesi
34 papers receiving 619 citations
Peers
Comparison fields: 5 of 109
- Molecular Biology 365
- Artificial Intelligence 131
- Physiology 121
- Computational Theory and Mathematics 109
- Psychiatry and Mental health 58
Countries citing papers authored by Erfan Younesi
This map shows the geographic impact of Erfan Younesi'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 Erfan Younesi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erfan Younesi more than expected).
Fields of papers citing papers by Erfan Younesi
This network shows the impact of papers produced by Erfan Younesi. 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 Erfan Younesi. The network helps show where Erfan Younesi may publish in the future.
Co-authorship network of co-authors of Erfan Younesi
This figure shows the co-authorship network connecting the top 25 collaborators of Erfan Younesi. A scholar is included among the top collaborators of Erfan Younesi 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 Erfan Younesi. Erfan Younesi 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 | 5 | |
| 3 | Lipid Profile and the Risk of Stroke: A Study from North of Iran | 4 |
| 4 | 19 | |
| 5 | 13 | |
| 6 | 10 | |
| 7 | 24 | |
| 8 | 3 | |
| 9 | 18 | |
| 10 | 11 | |
| 11 | 5 | |
| 12 | 35 | |
| 13 | 6 | |
| 14 | 7 | |
| 15 | 4 | |
| 16 | 27 | |
| 17 | 3 | |
| 18 | 32 | |
| 19 | 7 | |
| 20 | 2 |
About Erfan Younesi
Erfan Younesi is a scholar working on Computational Theory and Mathematics, Neurology and Molecular Biology, having authored 35 papers that have together received 643 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (18 papers), Biomedical Text Mining and Ontologies (15 papers) and Computational Drug Discovery Methods (13 papers). The work is most often cited by research in Computational Theory and Mathematics (109 citations), Molecular Biology (365 citations) and Neurology (40 citations). Erfan Younesi has collaborated with scholars based in Germany, United Kingdom and Iran. Frequent co-authors include Martin Hofmann‐Apitius, B. G. Müller, Alpha Tom Kodamullil, Leila Shahmoradi, Natalia Novac, Michael T. Heneka, Harald Hampel, Stanley Durrleman, Valentina Escott‐Price and Simone Lista. Their work appears in journals such as SHILAP Revista de lepidopterología, 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.