David Medina-Ortiz
- Modeling and Simulation top 5%
- Molecular Biology
- Infectious Diseases
- Economics and Econometrics
- Microbiology
- Co-authors
- Álvaro Olivera‐NappaSebastián ContrerasMehdi D. DavariMarcelo A. NavarreteClaudia P. SaavedraCarlos ConcaJuan A. AsenjoPaola Cicatiello
- Topics
- SARS-CoV-2 and COVID-19 Research (6 papers)COVID-19 epidemiological studies (5 papers)Machine Learning in Bioinformatics (4 papers)
- Partner nations
- ChileGermanyUnited States
In The Last Decade
David Medina-Ortiz
18 papers receiving 274 citations
Hit Papers
Peers
Comparison fields: 5 of 88
- Modeling and Simulation 95
- Molecular Biology 87
- Infectious Diseases 55
- Economics and Econometrics 31
- Microbiology 27
Countries citing papers authored by David Medina-Ortiz
This map shows the geographic impact of David Medina-Ortiz'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 David Medina-Ortiz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Medina-Ortiz more than expected).
Fields of papers citing papers by David Medina-Ortiz
This network shows the impact of papers produced by David Medina-Ortiz. 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 David Medina-Ortiz. The network helps show where David Medina-Ortiz may publish in the future.
Co-authorship network of co-authors of David Medina-Ortiz
This figure shows the co-authorship network connecting the top 25 collaborators of David Medina-Ortiz. A scholar is included among the top collaborators of David Medina-Ortiz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Medina-Ortiz. David Medina-Ortiz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptidesbreakdown → | 55 |
| 3 | 7 | |
| 4 | 6 | |
| 5 | 16 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 9 | |
| 9 | 4 | |
| 10 | 7 | |
| 11 | 1 | |
| 12 | 11 | |
| 13 | 23 | |
| 14 | 60 | |
| 15 | 19 | |
| 16 | 10 | |
| 17 | 19 | |
| 18 | 16 | |
| 19 | 11 | |
| 20 | 10 |
About David Medina-Ortiz
David Medina-Ortiz is a scholar working on Modeling and Simulation, Microbiology and Infectious Diseases, having authored 20 papers that have together received 285 indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (6 papers), COVID-19 epidemiological studies (5 papers) and Machine Learning in Bioinformatics (4 papers). The work is most often cited by research in Modeling and Simulation (95 citations), Microbiology (27 citations) and Infectious Diseases (55 citations). David Medina-Ortiz has collaborated with scholars based in Chile, Germany and United States. Frequent co-authors include Álvaro Olivera‐Nappa, Sebastián Contreras, Mehdi D. Davari, Marcelo A. Navarrete, Claudia P. Saavedra, Carlos Conca, Juan A. Asenjo, Paola Cicatiello, María Elena Lienqueo and Diego Fernández. Their work appears in journals such as Scientific Reports, ACS Catalysis 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.