Rave Harpaz
- Toxicology top 0.05%
- Pharmacovigilance and Adverse Drug Reactions 26
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods 18
- Geriatrics and Gerontology top 5%
- Pharmacology top 2%
- Drug-Induced Adverse Reactions 4
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- Biomedical Text Mining and Ontologies 10
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- Biosimilars and Bioanalytical Methods 5
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- Statistical Methods in Clinical Trials 3
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- Advanced Clustering Algorithms Research 3
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- Tuberculosis Research and Epidemiology 2
- Co-authors
- Nigam H. ShahCarol FriedmanWilliam DuMouchelHerbert ChasePaea LePenduPatrick RyanAnna Bauer‐MehrenSantiago Vilar
- Partner nations
- United StatesSpainUnited Kingdom
In The Last Decade
Rave Harpaz
34 papers receiving 2.0k citations
Peers
Comparison fields: 5 of 119
- Toxicology 1.1k
- Computational Theory and Mathematics 884
- Health Information Management 130
- Geriatrics and Gerontology 105
- Pharmacology 221
Countries citing papers authored by Rave Harpaz
This map shows the geographic impact of Rave Harpaz'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 Rave Harpaz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rave Harpaz more than expected).
Fields of papers citing papers by Rave Harpaz
This network shows the impact of papers produced by Rave Harpaz. 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 Rave Harpaz. The network helps show where Rave Harpaz may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Rave Harpaz, 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 | 2022 | 15 | |
| 2 | Extracting Positive Mentions of Adverse Drug Reactions from Product Labels using a Machine Learning Centric Approach. | 2017 | 1 |
| 3 | 2017 | 28 | |
| 4 | 2015 | 45 | |
| 5 | 2015 | 18 | |
| 6 | 2015 | 32 | |
| 7 | 2014 | 164 | |
| 8 | 2014 | 69 | |
| 9 | 2014 | 33 | |
| 10 | 2013 | 124 | |
| 11 | 2013 | 125 | |
| 12 | 2013 | 240 | |
| 13 | 2012 | 276 | |
| 14 | 2012 | 201 | |
| 15 | 2012 | 22 | |
| 16 | 2011 | 50 | |
| 17 | 2010 | 57 | |
| 18 | 2010 | 140 | |
| 19 | Model-based linear manifold clustering | 2008 | 1 |
| 20 | 2006 | 14 |
About Rave Harpaz
Rave Harpaz is a scholar working on Toxicology, Computational Theory and Mathematics and Health Information Management, having authored 34 papers that have together received 2.0k indexed citations. Recurring topics across this work include Pharmacovigilance and Adverse Drug Reactions (26 papers), Computational Drug Discovery Methods (18 papers), Biomedical Text Mining and Ontologies (10 papers), Biosimilars and Bioanalytical Methods (5 papers), Drug-Induced Adverse Reactions (4 papers), Statistical Methods in Clinical Trials (3 papers), Advanced Clustering Algorithms Research (3 papers) and Tuberculosis Research and Epidemiology (2 papers). The work is most often cited by research in Toxicology (1.1k citations), Computational Theory and Mathematics (884 citations) and Health Information Management (130 citations). Rave Harpaz has collaborated with scholars based in United States, Spain and United Kingdom. Frequent co-authors include Nigam H. Shah, Carol Friedman, William DuMouchel, Herbert Chase, Paea LePendu, Patrick Ryan, Anna Bauer‐Mehren, Santiago Vilar, C Friedman and Raúl Rabadán. Their work appears in journals such as PLoS ONE, BMC Bioinformatics and Pattern Recognition.
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.