Sarwan Ali
Impact in
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- SARS-CoV-2 and COVID-19 Research
Papers in
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- Machine Learning in Bioinformatics 17
- Fractal and DNA sequence analysis 8
- Genomics and Phylogenetic Studies 6
- Gene expression and cancer classification 4
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- SARS-CoV-2 and COVID-19 Research 7
- Co-authors
- Murray Patterson (22 shared papers)Imdadullah Khan (9 shared papers)Naveed Arshad (3 shared papers)Alex Zelikovsky (2 shared papers)Pin‐Yu Chen (1 shared paper)Simone Ciccolella (2 shared papers)Gianluca Della Vedova (2 shared papers)Babatunde Kazeem Bello (1 shared paper)
- Journals
- Journal of Computational Biology (3 papers)Biology (2 papers)Information Sciences (1 paper)Scientific Reports (1 paper)Knowledge-Based Systems (1 paper)
- Partner nations
- United StatesPakistanItaly
In The Last Decade
Sarwan Ali
25 papers receiving 154 citations
Peers
Comparison fields: 5 of 49
- Health Informatics 3
- Infectious Diseases 38
- Molecular Biology 89
- Radiology, Nuclear Medicine and Imaging 27
- Artificial Intelligence 37
Countries citing papers authored by Sarwan Ali
This map shows the geographic impact of Sarwan Ali'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 Sarwan Ali with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sarwan Ali more than expected).
Fields of papers citing papers by Sarwan Ali
This network shows the impact of papers produced by Sarwan Ali. 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 Sarwan Ali. The network helps show where Sarwan Ali may publish in the future.
Co-authors
The 13 scholars most cited alongside Sarwan Ali, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 25 | |
| 2 | 2022 | 23 | |
| 3 | 2022 | 19 | |
| 4 | 2019 | 15 | |
| 5 | 2023 | 13 | |
| 6 | 2022 | 9 | |
| 7 | 2023 | 8 | |
| 8 | 2023 | 6 | |
| 9 | 2023 | 6 | |
| 10 | 2021 | 5 | |
| 11 | 2023 | 4 | |
| 12 | 2022 | 4 | |
| 13 | 2024 | 3 | |
| 14 | 2023 | 2 | |
| 15 | 2022 | 2 | |
| 16 | 2023 | 2 | |
| 17 | 2022 | 2 | |
| 18 | 2023 | 2 | |
| 19 | 2022 | 2 | |
| 20 | 2024 | 1 |
About Sarwan Ali
Sarwan Ali is a scholar working on Molecular Biology, Infectious Diseases, Artificial Intelligence, Computer Vision and Pattern Recognition and Ecology, having authored 31 papers that have together received 158 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (17 papers), Fractal and DNA sequence analysis (8 papers), SARS-CoV-2 and COVID-19 Research (7 papers), Genomics and Phylogenetic Studies (6 papers), Gene expression and cancer classification (4 papers), COVID-19 diagnosis using AI (3 papers), Bacteriophages and microbial interactions (3 papers) and Energy Load and Power Forecasting (3 papers). The work is most often cited by research in Health Informatics (3 citations), Infectious Diseases (38 citations), Molecular Biology (89 citations), Radiology, Nuclear Medicine and Imaging (27 citations) and Artificial Intelligence (37 citations). Sarwan Ali has collaborated with scholars based in United States, Pakistan and Italy. Frequent co-authors include Murray Patterson, Imdadullah Khan, Naveed Arshad, Alex Zelikovsky, Pin‐Yu Chen, Simone Ciccolella, Gianluca Della Vedova, Babatunde Kazeem Bello, Waseem Abbas and Mian Muhammad Awais. Their work appears in journals such as Journal of Computational Biology, Biology, Information Sciences, Scientific Reports and Knowledge-Based Systems.
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.