Elyas Sabeti
- Computational Theory and Mathematics top 2%
- Molecular Biology
- Materials Chemistry
- Artificial Intelligence
- Biomedical Engineering
- Co-authors
- Kayvan NajarianMaryam BagherianKai WangMaureen A. SartorZaneta Nikolovska‐ColeskaAnders Høst-MadsenJonathan GryakMichael W. Sjoding
- Topics
- Time Series Analysis and Forecasting (5 papers)Anomaly Detection Techniques and Applications (4 papers)Fractal and DNA sequence analysis (3 papers)
- Journals
- IEEE Transactions on Information TheoryBriefings in BioinformaticsIEEE Journal of Biomedical and Health Informatics
- Partner nations
- United StatesChina
In The Last Decade
Elyas Sabeti
16 papers receiving 415 citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Computational Theory and Mathematics 258
- Molecular Biology 227
- Materials Chemistry 78
- Artificial Intelligence 66
- Biomedical Engineering 42
Countries citing papers authored by Elyas Sabeti
This map shows the geographic impact of Elyas Sabeti'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 Elyas Sabeti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elyas Sabeti more than expected).
Fields of papers citing papers by Elyas Sabeti
This network shows the impact of papers produced by Elyas Sabeti. 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 Elyas Sabeti. The network helps show where Elyas Sabeti may publish in the future.
Co-authorship network of co-authors of Elyas Sabeti
This figure shows the co-authorship network connecting the top 25 collaborators of Elyas Sabeti. A scholar is included among the top collaborators of Elyas Sabeti 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 Elyas Sabeti. Elyas Sabeti 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 | 30 | |
| 3 | 13 | |
| 4 | 26 | |
| 5 | 4 | |
| 6 | 6 | |
| 7 | 7 | |
| 8 | 16 | |
| 9 | Machine learning approaches and databases for prediction of drug–target interaction: a survey paperbreakdown → | 296 |
| 10 | 1 | |
| 11 | 1 | |
| 12 | 2 | |
| 13 | 5 | |
| 14 | 7 | |
| 15 | 2 | |
| 16 | 7 |
About Elyas Sabeti
Elyas Sabeti is a scholar working on Signal Processing, Artificial Intelligence and Computational Theory and Mathematics, having authored 16 papers that have together received 424 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (5 papers), Anomaly Detection Techniques and Applications (4 papers) and Fractal and DNA sequence analysis (3 papers). The work is most often cited by research in Computational Theory and Mathematics (258 citations), Molecular Biology (227 citations) and Health Informatics (4 citations). Elyas Sabeti has collaborated with scholars based in United States and China. Frequent co-authors include Kayvan Najarian, Maryam Bagherian, Kai Wang, Maureen A. Sartor, Zaneta Nikolovska‐Coleska, Anders Høst-Madsen, Jonathan Gryak, Michael W. Sjoding, Harm Derksen and Chad B. Walton. Their work appears in journals such as IEEE Transactions on Information Theory, Briefings in Bioinformatics and IEEE Journal of Biomedical and Health Informatics.
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.