Aliuska Duardo-Sánchez
- Molecular Biology
- Computational Theory and Mathematics top 2%
- Pharmacology
- Organic Chemistry
- Statistical and Nonlinear Physics
- Co-authors
- Humberto González‐DíazCristian R. MunteanuFrancisco Prado-PradoLázaro G. Pérez‐MontotoGrace PatlewiczAlejandro PazosFlorencio M. UbeiraRiccardo Concu
- Topics
- Computational Drug Discovery Methods (14 papers)Bioinformatics and Genomic Networks (9 papers)Machine Learning in Bioinformatics (4 papers)
- Journals
- Journal of Theoretical BiologyCurrent Pharmaceutical DesignJournal of Chemical Information and Modeling
- Partner nations
- SpainPortugalUnited States
In The Last Decade
Aliuska Duardo-Sánchez
26 papers receiving 367 citations
Peers
Comparison fields: 5 of 79
- Molecular Biology 256
- Computational Theory and Mathematics 237
- Pharmacology 42
- Organic Chemistry 41
- Statistical and Nonlinear Physics 20
Countries citing papers authored by Aliuska Duardo-Sánchez
This map shows the geographic impact of Aliuska Duardo-Sánchez'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 Aliuska Duardo-Sánchez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aliuska Duardo-Sánchez more than expected).
Fields of papers citing papers by Aliuska Duardo-Sánchez
This network shows the impact of papers produced by Aliuska Duardo-Sánchez. 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 Aliuska Duardo-Sánchez. The network helps show where Aliuska Duardo-Sánchez may publish in the future.
Co-authorship network of co-authors of Aliuska Duardo-Sánchez
This figure shows the co-authorship network connecting the top 25 collaborators of Aliuska Duardo-Sánchez. A scholar is included among the top collaborators of Aliuska Duardo-Sánchez 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 Aliuska Duardo-Sánchez. Aliuska Duardo-Sánchez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 5 | |
| 5 | 8 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 47 | |
| 9 | 13 | |
| 10 | 28 | |
| 11 | 1 | |
| 12 | 36 | |
| 13 | 12 | |
| 14 | 9 | |
| 15 | 37 | |
| 16 | 44 | |
| 17 | Alignment-Free Models in Plant Genomics: Theoretical, Experimental, and Legal Issues | 1 |
| 18 | 35 | |
| 19 | 16 | |
| 20 | 27 |
About Aliuska Duardo-Sánchez
Aliuska Duardo-Sánchez is a scholar working on Computational Theory and Mathematics, Physical and Theoretical Chemistry and Statistical and Nonlinear Physics, having authored 28 papers that have together received 374 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (14 papers), Bioinformatics and Genomic Networks (9 papers) and Machine Learning in Bioinformatics (4 papers). The work is most often cited by research in Computational Theory and Mathematics (237 citations), Molecular Biology (256 citations) and Pharmacology (42 citations). Aliuska Duardo-Sánchez has collaborated with scholars based in Spain, Portugal and United States. Frequent co-authors include Humberto González‐Díaz, Cristian R. Munteanu, Francisco Prado-Prado, Lázaro G. Pérez‐Montoto, Grace Patlewicz, Alejandro Pazos, Florencio M. Ubeira, Riccardo Concu, Manuel Escobar and Gianni Podda. Their work appears in journals such as Journal of Theoretical Biology, Current Pharmaceutical Design and Journal of Chemical Information and Modeling.
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.