Farrokh Mehryary
- Molecular Biology top 5%
- Cancer Research top 5%
- Immunology top 10%
- Genetics top 5%
- Pulmonary and Respiratory Medicine top 10%
- Co-authors
- Sampo PyysaloKaterina NastouLars Juhl JensenChristian von MeringMikaela KoutrouliPeer BorkRebecca KirschNadezhda T. Doncheva
- Topics
- Biomedical Text Mining and Ontologies (9 papers)Bioinformatics and Genomic Networks (7 papers)Topic Modeling (6 papers)
- Journals
- Nucleic Acids ResearchBioinformaticsJournal of the American Medical Informatics Association
- Partner nations
- FinlandDenmarkUnited Kingdom
In The Last Decade
Farrokh Mehryary
12 papers receiving 3.8k citations
Hit Papers
Peers
Comparison fields: 5 of 146
- Molecular Biology 2.1k
- Cancer Research 460
- Immunology 370
- Genetics 347
- Pulmonary and Respiratory Medicine 342
Countries citing papers authored by Farrokh Mehryary
This map shows the geographic impact of Farrokh Mehryary'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 Farrokh Mehryary with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Farrokh Mehryary more than expected).
Fields of papers citing papers by Farrokh Mehryary
This network shows the impact of papers produced by Farrokh Mehryary. 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 Farrokh Mehryary. The network helps show where Farrokh Mehryary may publish in the future.
Co-authorship network of co-authors of Farrokh Mehryary
This figure shows the co-authorship network connecting the top 25 collaborators of Farrokh Mehryary. A scholar is included among the top collaborators of Farrokh Mehryary 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 Farrokh Mehryary. Farrokh Mehryary 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 | 1 | |
| 3 | The STRING database in 2025: protein networks with directionality of regulationbreakdown → | 93 |
| 4 | 7 | |
| 5 | 2 | |
| 6 | 10 | |
| 7 | The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interestbreakdown → | 3589 |
| 8 | Entity-Pair Embeddings for Improving Relation Extraction in the Biomedical Domain. | 1 |
| 9 | 17 | |
| 10 | 61 | |
| 11 | 11 | |
| 12 | 10 | |
| 13 | 0 | |
| 14 | 25 | |
| 15 | 0 |
About Farrokh Mehryary
Farrokh Mehryary is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology, having authored 15 papers that have together received 3.8k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (9 papers), Bioinformatics and Genomic Networks (7 papers) and Topic Modeling (6 papers). The work is most often cited by research in Molecular Biology (2.1k citations), Cancer Research (460 citations) and Biological Psychiatry (48 citations). Farrokh Mehryary has collaborated with scholars based in Finland, Denmark and United Kingdom. Frequent co-authors include Sampo Pyysalo, Katerina Nastou, Lars Juhl Jensen, Christian von Mering, Mikaela Koutrouli, Peer Bork, Rebecca Kirsch, Nadezhda T. Doncheva, Damian Szklarczyk and Tao Fang. Their work appears in journals such as Nucleic Acids Research, Bioinformatics and Journal of the American Medical Informatics Association.
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.