Marco S. Nobile
- Artificial Intelligence top 2%
- Molecular Biology
- Computational Theory and Mathematics top 2%
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Co-authors
- Paolo CazzanigaDaniela BesozziGiancarlo MauriAndrea TangherloniSimone SpolaorLeonardo RundoRiccardo ColomboGabriella Pasi
- Topics
- Gene Regulatory Network Analysis (32 papers)Evolutionary Algorithms and Applications (20 papers)Microbial Metabolic Engineering and Bioproduction (19 papers)
- Journals
- SHILAP Revista de lepidopterologíaBioinformaticsPLoS ONE
- Partner nations
- ItalyNetherlandsUnited States
In The Last Decade
Marco S. Nobile
85 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 176
- Artificial Intelligence 521
- Molecular Biology 401
- Computational Theory and Mathematics 205
- Computer Vision and Pattern Recognition 134
- Biomedical Engineering 84
Countries citing papers authored by Marco S. Nobile
This map shows the geographic impact of Marco S. Nobile'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 Marco S. Nobile with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco S. Nobile more than expected).
Fields of papers citing papers by Marco S. Nobile
This network shows the impact of papers produced by Marco S. Nobile. 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 Marco S. Nobile. The network helps show where Marco S. Nobile may publish in the future.
Co-authorship network of co-authors of Marco S. Nobile
This figure shows the co-authorship network connecting the top 25 collaborators of Marco S. Nobile. A scholar is included among the top collaborators of Marco S. Nobile 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 Marco S. Nobile. Marco S. Nobile 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 | 0 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 10 | |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 5 | |
| 10 | 7 | |
| 11 | 15 | |
| 12 | 0 | |
| 13 | 1 | |
| 14 | 1 | |
| 15 | 15 | |
| 16 | 39 | |
| 17 | 1 | |
| 18 | 9 | |
| 19 | 16 | |
| 20 | The foundation of Evolutionary Petri Nets | 3 |
About Marco S. Nobile
Marco S. Nobile is a scholar working on Health Informatics, Artificial Intelligence and Computational Theory and Mathematics, having authored 96 papers that have together received 1.3k indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (32 papers), Evolutionary Algorithms and Applications (20 papers) and Microbial Metabolic Engineering and Bioproduction (19 papers). The work is most often cited by research in Artificial Intelligence (521 citations), Computational Theory and Mathematics (205 citations) and Health Informatics (13 citations). Marco S. Nobile has collaborated with scholars based in Italy, Netherlands and United States. Frequent co-authors include Paolo Cazzaniga, Daniela Besozzi, Giancarlo Mauri, Andrea Tangherloni, Simone Spolaor, Leonardo Rundo, Riccardo Colombo, Gabriella Pasi, Uzay Kaymak and Carmelo Militello. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.
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.