Luca Cappelletti
Impact in
- Health Informatics top 10%
Papers in
-
- Bioinformatics and Genomic Networks 4
- Biomedical Text Mining and Ontologies 3
- Machine Learning in Bioinformatics 1
- Genomics and Phylogenetic Studies 1
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- Advanced Graph Neural Networks 4
- Co-authors
- Giorgio Valentini (8 shared papers)Tommaso Fontana (7 shared papers)Peter N. Robinson (7 shared papers)Justin Reese (6 shared papers)Elena Casiraghi (6 shared papers)Vida Ravanmehr (5 shared papers)Marcin P. Joachimiak (4 shared papers)Chris Mungall (5 shared papers)
- Journals
- Bioorganic & Medicinal Chemistry (1 paper)ACS Central Science (1 paper)BMC Bioinformatics (1 paper)NAR Genomics and Bioinformatics (1 paper)Nature Computational Science (1 paper)
- Partner nations
- ItalyUnited StatesGermany
In The Last Decade
Luca Cappelletti
14 papers receiving 213 citations
Peers
Comparison fields: 5 of 83
- Health Informatics 12
- Health Information Management 13
- Artificial Intelligence 85
- Radiology, Nuclear Medicine and Imaging 46
- Modeling and Simulation 9
Countries citing papers authored by Luca Cappelletti
This map shows the geographic impact of Luca Cappelletti'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 Luca Cappelletti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luca Cappelletti more than expected).
Fields of papers citing papers by Luca Cappelletti
This network shows the impact of papers produced by Luca Cappelletti. 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 Luca Cappelletti. The network helps show where Luca Cappelletti may publish in the future.
Co-authors
The 25 scholars most cited alongside Luca Cappelletti, 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 | 2020 | 56 | |
| 2 | 2020 | 54 | |
| 3 | 2012 | 15 | |
| 4 | 2019 | 15 | |
| 5 | 2023 | 14 | |
| 6 | 2024 | 11 | |
| 7 | 2020 | 11 | |
| 8 | 2015 | 10 | |
| 9 | 2020 | 9 | |
| 10 | 1998 | 9 | |
| 11 | 2021 | 4 | |
| 12 | 2024 | 4 | |
| 13 | 2022 | 3 | |
| 14 | 2020 | 2 |
About Luca Cappelletti
Luca Cappelletti is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Organic Chemistry and Sociology and Political Science, having authored 14 papers that have together received 217 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (4 papers), Bioinformatics and Genomic Networks (4 papers), Biomedical Text Mining and Ontologies (3 papers), Computational Drug Discovery Methods (2 papers), Machine Learning in Bioinformatics (1 paper), COVID-19 diagnosis using AI (1 paper), Multisensory perception and integration (1 paper) and Genomics and Phylogenetic Studies (1 paper). The work is most often cited by research in Health Informatics (12 citations), Health Information Management (13 citations), Artificial Intelligence (85 citations), Radiology, Nuclear Medicine and Imaging (46 citations) and Modeling and Simulation (9 citations). Luca Cappelletti has collaborated with scholars based in Italy, United States and Germany. Frequent co-authors include Giorgio Valentini, Tommaso Fontana, Peter N. Robinson, Justin Reese, Elena Casiraghi, Vida Ravanmehr, Marcin P. Joachimiak, Chris Mungall, Marco Frasca and Tiffany J. Callahan. Their work appears in journals such as Bioorganic & Medicinal Chemistry, ACS Central Science, BMC Bioinformatics, NAR Genomics and Bioinformatics and Nature Computational Science.
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.