Mario Pavone
- Biomedical Engineering
- Artificial Intelligence top 10%
- Molecular Biology
- Immunology
- Computational Theory and Mathematics top 10%
- Co-authors
- Vincenzo CutelloGiuseppe NicosiaJon TimmisCarlos A. Coello CoelloGiuseppe NarzisiAlireza OsarehBita ShadgarDavid A. Pelta
- Topics
- Artificial Immune Systems Applications (18 papers)Metaheuristic Optimization Algorithms Research (11 papers)vaccines and immunoinformatics approaches (6 papers)
- Partner nations
- ItalyUnited KingdomMexico
In The Last Decade
Mario Pavone
46 papers receiving 456 citations
Peers
Comparison fields: 5 of 78
- Biomedical Engineering 208
- Artificial Intelligence 192
- Molecular Biology 174
- Immunology 84
- Computational Theory and Mathematics 66
Countries citing papers authored by Mario Pavone
This map shows the geographic impact of Mario Pavone'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 Mario Pavone with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Pavone more than expected).
Fields of papers citing papers by Mario Pavone
This network shows the impact of papers produced by Mario Pavone. 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 Mario Pavone. The network helps show where Mario Pavone may publish in the future.
Co-authorship network of co-authors of Mario Pavone
This figure shows the co-authorship network connecting the top 25 collaborators of Mario Pavone. A scholar is included among the top collaborators of Mario Pavone 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 Mario Pavone. Mario Pavone 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 | 2 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 29 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | 9 | |
| 9 | 2 | |
| 10 | 6 | |
| 11 | 4 | |
| 12 | 1 | |
| 13 | 7 | |
| 14 | 3 | |
| 15 | Effective calibration of artificial gene regulatory networks. | 1 |
| 16 | 17 | |
| 17 | 5 | |
| 18 | Nature Inspired Cooperative Strategies for Optimization (NICSO 2007) (Studies in Computational Intelligence) (Studies in Computational Intelligence) XXXX | 10 |
| 19 | Aligning multiple protein sequences by hybrid clonal selection algorithm with insert-remove-gaps and blockshuffling operators | 2 |
| 20 | 23 |
About Mario Pavone
Mario Pavone is a scholar working on Industrial and Manufacturing Engineering, Biomedical Engineering and Artificial Intelligence, having authored 47 papers that have together received 487 indexed citations. Recurring topics across this work include Artificial Immune Systems Applications (18 papers), Metaheuristic Optimization Algorithms Research (11 papers) and vaccines and immunoinformatics approaches (6 papers). The work is most often cited by research in Artificial Intelligence (192 citations), Biomedical Engineering (208 citations) and Computational Theory and Mathematics (66 citations). Mario Pavone has collaborated with scholars based in Italy, United Kingdom and Mexico. Frequent co-authors include Vincenzo Cutello, Giuseppe Nicosia, Jon Timmis, Carlos A. Coello Coello, Giuseppe Narzisi, Alireza Osareh, Bita Shadgar, David A. Pelta, Natalio Krasnogor and Murat Aslan. Their work appears in journals such as Nucleic Acids Research, IEEE Transactions on Evolutionary Computation and Applied Soft Computing.
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.