Hani Neuvirth
Impact in
- Virology top 10%
- HIV Research and Treatment
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- Computational Drug Discovery Methods
Papers in ⓘ
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- Protein Structure and Dynamics 5
- Bioinformatics and Genomic Networks 2
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- Enzyme Structure and Function 4
- Co-authors
- Gideon Schreiber (5 shared papers)Ran Raz (1 shared paper)Mati Cohen (2 shared papers)Dana Reichmann (2 shared papers)Ofer Rahat (1 shared paper)Michal Rosen‐Zvi (3 shared papers)Ehud Aharoni (4 shared papers)Kay E. Gottschalk (1 shared paper)
- Journals
- Genetic Epidemiology (1 paper)Protein Engineering Design and Selection (1 paper)Big Data (1 paper)Proteins Structure Function and Bioinformatics (1 paper)Journal of Molecular Biology (1 paper)
- Partner nations
- IsraelUnited StatesItaly
In The Last Decade
Hani Neuvirth
12 papers receiving 699 citations
Peers
Comparison fields: 5 of 90
- Virology 56
- Computational Theory and Mathematics 181
- Molecular Biology 554
- Health Information Management 24
- Materials Chemistry 175
Countries citing papers authored by Hani Neuvirth
This map shows the geographic impact of Hani Neuvirth'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 Hani Neuvirth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hani Neuvirth more than expected).
Fields of papers citing papers by Hani Neuvirth
This network shows the impact of papers produced by Hani Neuvirth. 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 Hani Neuvirth. The network helps show where Hani Neuvirth may publish in the future.
Co-authors
The 25 scholars most cited alongside Hani Neuvirth, 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 | 2004 | 344 | |
| 2 | 2007 | 159 | |
| 3 | 2008 | 47 | |
| 4 | 2008 | 40 | |
| 5 | 2008 | 35 | |
| 6 | 2011 | 35 | |
| 7 | 2004 | 24 | |
| 8 | 2007 | 17 | |
| 9 | 2016 | 6 | |
| 10 | 2010 | 5 | |
| 11 | 2011 | 2 | |
| 12 | 2014 | 2 |
About Hani Neuvirth
Hani Neuvirth is a scholar working on Molecular Biology, Materials Chemistry, Computational Theory and Mathematics, Statistics and Probability and Infectious Diseases, having authored 12 papers that have together received 716 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (5 papers), Enzyme Structure and Function (4 papers), Computational Drug Discovery Methods (3 papers), Statistical Methods in Clinical Trials (3 papers), HIV Research and Treatment (2 papers), Bioinformatics and Genomic Networks (2 papers), HIV/AIDS drug development and treatment (2 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (1 paper). The work is most often cited by research in Virology (56 citations), Computational Theory and Mathematics (181 citations), Molecular Biology (554 citations), Health Information Management (24 citations) and Materials Chemistry (175 citations). Hani Neuvirth has collaborated with scholars based in Israel, United States and Italy. Frequent co-authors include Gideon Schreiber, Ran Raz, Mati Cohen, Dana Reichmann, Ofer Rahat, Michal Rosen‐Zvi, Ehud Aharoni, Kay E. Gottschalk, Daniel Struck and Martin Köhn. Their work appears in journals such as Genetic Epidemiology, Protein Engineering Design and Selection, Big Data, Proteins Structure Function and Bioinformatics and Journal of Molecular Biology.
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.