Abdollah Dehzangi
- Health Informatics top 2%
- Molecular Biology top 2%
- Machine Learning in Bioinformatics 72
- RNA and protein synthesis mechanisms 38
- Genomics and Phylogenetic Studies 32
- Protein Structure and Dynamics 29
- vaccines and immunoinformatics approaches 7
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- Computational Drug Discovery Methods 12
- Neurology top 5%
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- Antimicrobial Peptides and Activities 5
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- Enzyme Structure and Function 4
- Co-authors
- Alok SharmaKuldip K. PaliwalJames LyonsAbdul SattarRhys HeffernanSwakkhar ShatabdaYaoqi ZhouShahab S. Band
- Partner nations
- United StatesAustraliaFiji
In The Last Decade
Abdollah Dehzangi
99 papers receiving 3.4k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Health Informatics 71
- Molecular Biology 2.6k
- Computational Theory and Mathematics 472
- Health Information Management 127
- Neurology 171
Countries citing papers authored by Abdollah Dehzangi
This map shows the geographic impact of Abdollah Dehzangi'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 Abdollah Dehzangi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Abdollah Dehzangi more than expected).
Fields of papers citing papers by Abdollah Dehzangi
This network shows the impact of papers produced by Abdollah Dehzangi. 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 Abdollah Dehzangi. The network helps show where Abdollah Dehzangi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Abdollah Dehzangi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 1 | |
| 5 | 2021 | 3 | |
| 6 | 2021 | 9 | |
| 7 | 2021 | 1 | |
| 8 | 2020 | 199 | |
| 9 | 2020 | 23 | |
| 10 | 2019 | 15 | |
| 11 | 2019 | 49 | |
| 12 | 2019 | 16 | |
| 13 | 2018 | 20 | |
| 14 | 2018 | 30 | |
| 15 | 2018 | 28 | |
| 16 | 2017 | 80 | |
| 17 | 2017 | 41 | |
| 18 | 2013 | 43 | |
| 19 | Using random forest for protein fold prediction problem: An empirical study | 2010 | 38 |
| 20 | Enhancing Protein Fold Prediction Accuracy Using an Ensemble of Different Classifiers | 2009 | 21 |
About Abdollah Dehzangi
Abdollah Dehzangi is a scholar working on Health Informatics, Molecular Biology and Computational Theory and Mathematics, having authored 102 papers that have together received 3.5k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (72 papers), RNA and protein synthesis mechanisms (38 papers), Genomics and Phylogenetic Studies (32 papers), Protein Structure and Dynamics (29 papers), Computational Drug Discovery Methods (12 papers), vaccines and immunoinformatics approaches (7 papers), Antimicrobial Peptides and Activities (5 papers) and Enzyme Structure and Function (4 papers). The work is most often cited by research in Health Informatics (71 citations), Molecular Biology (2.6k citations) and Computational Theory and Mathematics (472 citations). Abdollah Dehzangi has collaborated with scholars based in United States, Australia and Fiji. Frequent co-authors include Alok Sharma, Kuldip K. Paliwal, James Lyons, Abdul Sattar, Rhys Heffernan, Swakkhar Shatabda, Yaoqi Zhou, Shahab S. Band, Tatsuhiko Tsunoda and Yuedong Yang.
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.