Babak Sokouti
- Molecular Biology
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Computational Theory and Mathematics top 10%
- Surgery
- Co-authors
- Solmaz AsnaashariSiavoush DastmalchiMohsen SokoutiSaeid PashazadehAli Dastranj TabriziMaryam Hamzeh‐MivehroudMorteza GhojazadehRamin Sadeghi
- Topics
- Computational Drug Discovery Methods (12 papers)AI in cancer detection (6 papers)Lung Cancer Diagnosis and Treatment (5 papers)
- Cited by
- Computational Theory and MathematicsPathology and Forensic MedicineComputer Vision and Pattern Recognition
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsJournal of Chemical Information and Modeling
In The Last Decade
Babak Sokouti
58 papers receiving 477 citations
Peers
Comparison fields: 5 of 97
- Molecular Biology 146
- Artificial Intelligence 78
- Computer Vision and Pattern Recognition 73
- Computational Theory and Mathematics 70
- Surgery 69
Countries citing papers authored by Babak Sokouti
This map shows the geographic impact of Babak Sokouti'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 Babak Sokouti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Babak Sokouti more than expected).
Fields of papers citing papers by Babak Sokouti
This network shows the impact of papers produced by Babak Sokouti. 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 Babak Sokouti. The network helps show where Babak Sokouti may publish in the future.
Co-authorship network of co-authors of Babak Sokouti
This figure shows the co-authorship network connecting the top 25 collaborators of Babak Sokouti. A scholar is included among the top collaborators of Babak Sokouti 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 Babak Sokouti. Babak Sokouti 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 | 2 | |
| 3 | 1 | |
| 4 | 10 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 37 | |
| 8 | 2 | |
| 9 | 0 | |
| 10 | 3 | |
| 11 | 1 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 3 | |
| 15 | 2 | |
| 16 | 25 | |
| 17 | Primary role of EUS, CT, and Esophagoscopy in Diagnosing Multiple Giant Leiomyoma of the Esophagus: a Literature Review | 1 |
| 18 | 3 | |
| 19 | 1 | |
| 20 | 5 |
About Babak Sokouti
Babak Sokouti is a scholar working on Computational Theory and Mathematics, Modeling and Simulation and Pulmonary and Respiratory Medicine, having authored 66 papers that have together received 493 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (12 papers), AI in cancer detection (6 papers) and Lung Cancer Diagnosis and Treatment (5 papers). The work is most often cited by research in Computational Theory and Mathematics (70 citations), Pathology and Forensic Medicine (67 citations) and Computer Vision and Pattern Recognition (73 citations). Babak Sokouti has collaborated with scholars based in Iran, Italy and Cyprus. Frequent co-authors include Solmaz Asnaashari, Siavoush Dastmalchi, Mohsen Sokouti, Saeid Pashazadeh, Ali Dastranj Tabrizi, Maryam Hamzeh‐Mivehroud, Morteza Ghojazadeh, Ramin Sadeghi, Ali Zakerolhosseini and Raffaele Pezzani. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of Chemical Information and Modeling.
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.