Diego Galeano
Impact in
- Toxicology top 10%
- Pharmacovigilance and Adverse Drug Reactions
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- Computational Drug Discovery Methods
Papers in
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- Computational Drug Discovery Methods 7
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- Stroke Rehabilitation and Recovery 4
- Co-authors
- Alberto Paccanaro (7 shared papers)Shantao Li (1 shared paper)Mark B. Gerstein (1 shared paper)Luca Cernuzzi (2 shared papers)Mateo Torres (1 shared paper)F. Brunetti (3 shared papers)Diego Torricelli (3 shared papers)Francisco J. Enguita (4 shared papers)
- Journals
- Nature Communications (3 papers)Theranostics (1 paper)Patterns (1 paper)Cell Reports Methods (1 paper)Cell Reports (1 paper)
- Partner nations
- ParaguayUnited StatesUnited Kingdom
In The Last Decade
Diego Galeano
19 papers receiving 480 citations
Diego Galeano's Hit Papers
Peers
Comparison fields: 5 of 99
- Toxicology 34
- Computational Theory and Mathematics 107
- Neurology 79
- Infectious Diseases 94
- Cancer Research 63
Countries citing papers authored by Diego Galeano
This map shows the geographic impact of Diego Galeano'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 Diego Galeano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Galeano more than expected).
Fields of papers citing papers by Diego Galeano
This network shows the impact of papers produced by Diego Galeano. 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 Diego Galeano. The network helps show where Diego Galeano may publish in the future.
Co-authors
The 25 scholars most cited alongside Diego Galeano, 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Core mitochondrial genes are down-regulated during SARS-CoV-2 infection of rodent and human hosts Hit paper breakdown → | 2023 | 124 |
| 2 | 2020 | 102 | |
| 3 | 2021 | 68 | |
| 4 | 2021 | 37 | |
| 5 | 2022 | 31 | |
| 6 | 2023 | 27 | |
| 7 | 2022 | 23 | |
| 8 | 2018 | 18 | |
| 9 | 2014 | 15 | |
| 10 | 2024 | 14 | |
| 11 | 2024 | 10 | |
| 12 | 2016 | 6 | |
| 13 | 2011 | 5 | |
| 14 | 2024 | 5 | |
| 15 | 2013 | 2 | |
| 16 | 1998 | 1 | |
| 17 | 2015 | 1 | |
| 18 | 2017 | 1 | |
| 19 | 2021 | 1 | |
| 20 | 2016 | 0 |
About Diego Galeano
Diego Galeano is a scholar working on Computational Theory and Mathematics, Rehabilitation, Cancer Research, Molecular Biology and Infectious Diseases, having authored 21 papers that have together received 491 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), Stroke Rehabilitation and Recovery (4 papers), Bioinformatics and Genomic Networks (3 papers), MicroRNA in disease regulation (3 papers), Biomedical Text Mining and Ontologies (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Microbial Natural Products and Biosynthesis (2 papers) and Cancer-related molecular mechanisms research (2 papers). The work is most often cited by research in Toxicology (34 citations), Computational Theory and Mathematics (107 citations), Neurology (79 citations), Infectious Diseases (94 citations) and Cancer Research (63 citations). Diego Galeano has collaborated with scholars based in Paraguay, United States and United Kingdom. Frequent co-authors include Alberto Paccanaro, Shantao Li, Mark B. Gerstein, Luca Cernuzzi, Mateo Torres, F. Brunetti, Diego Torricelli, Francisco J. Enguita, Afshin Beheshti and José Luis Pons. Their work appears in journals such as Nature Communications, Theranostics, Patterns, Cell Reports Methods and Cell Reports.
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.