Álvaro Olivera‐Nappa
- Modeling and Simulation top 2%
- COVID-19 epidemiological studies 7
- Filtration and Separation top 10%
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- Antimicrobial Peptides and Activities 4
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- Iron Metabolism and Disorders 5
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- SARS-CoV-2 and COVID-19 Research 9
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- Machine Learning in Bioinformatics 4
- Chemical Synthesis and Analysis 3
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- COVID-19 Pandemic Impacts 3
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- Trypanosoma species research and implications 3
- Co-authors
- David Medina-OrtizSebastián ContrerasJuan A. AsenjoZiomara P. GerdtzenCarlos ConcaJ. Cristian SalgadoBárbara A. AndrewsMarco T. Núñez
- Journals
- PLoS ONE (2 papers)Scientific Reports (1 paper)International Journal of Molecular Sciences (1 paper)
- Partner nations
- ChileGermanyUnited States
In The Last Decade
Álvaro Olivera‐Nappa
40 papers receiving 644 citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Modeling and Simulation 108
- Filtration and Separation 17
- Endocrinology, Diabetes and Metabolism 77
- Microbiology 27
- Hematology 46
Countries citing papers authored by Álvaro Olivera‐Nappa
This map shows the geographic impact of Álvaro Olivera‐Nappa'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 Álvaro Olivera‐Nappa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Álvaro Olivera‐Nappa more than expected).
Fields of papers citing papers by Álvaro Olivera‐Nappa
This network shows the impact of papers produced by Álvaro Olivera‐Nappa. 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 Álvaro Olivera‐Nappa. The network helps show where Álvaro Olivera‐Nappa may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Álvaro Olivera‐Nappa, 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 | 2024 | 2 | |
| 2 | Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptidesbreakdown → | 2024 | 55 |
| 3 | 2024 | 7 | |
| 4 | 2024 | 6 | |
| 5 | 2024 | 0 | |
| 6 | 2023 | 4 | |
| 7 | 2022 | 7 | |
| 8 | 2022 | 7 | |
| 9 | 2022 | 1 | |
| 10 | 2021 | 5 | |
| 11 | 2021 | 8 | |
| 12 | 2021 | 11 | |
| 13 | 2020 | 60 | |
| 14 | 2020 | 19 | |
| 15 | 2020 | 10 | |
| 16 | 2020 | 17 | |
| 17 | 2017 | 9 | |
| 18 | 2010 | 15 | |
| 19 | 2004 | 22 | |
| 20 | 2004 | 22 |
About Álvaro Olivera‐Nappa
Álvaro Olivera‐Nappa is a scholar working on Modeling and Simulation, Microbiology and Infectious Diseases, having authored 41 papers that have together received 661 indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (9 papers), COVID-19 epidemiological studies (7 papers), Iron Metabolism and Disorders (5 papers), Machine Learning in Bioinformatics (4 papers), Antimicrobial Peptides and Activities (4 papers), COVID-19 Pandemic Impacts (3 papers), Trypanosoma species research and implications (3 papers) and Chemical Synthesis and Analysis (3 papers). The work is most often cited by research in Modeling and Simulation (108 citations), Filtration and Separation (17 citations) and Endocrinology, Diabetes and Metabolism (77 citations). Álvaro Olivera‐Nappa has collaborated with scholars based in Chile, Germany and United States. Frequent co-authors include David Medina-Ortiz, Sebastián Contreras, Juan A. Asenjo, Ziomara P. Gerdtzen, Carlos Conca, J. Cristian Salgado, Bárbara A. Andrews, Marco T. Núñez, Pilar Vigil and Mónica Soler. Their work appears in journals such as PLoS ONE, Scientific Reports and International Journal of Molecular Sciences.
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.