Matteo Barbieri
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
-
- Landslides and related hazards
Papers in
- Co-authors
- Luoting ZhuangMing Y. LuDrew F. K. WilliamsonMuhammad ShabanBowen ChenAnurag VaidyaTiffany ChenChengkuan Chen
In The Last Decade
Matteo Barbieri
30 papers receiving 909 citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Health Informatics 68
- Management, Monitoring, Policy and Law 161
- Horticulture 13
- Biophysics 67
- Radiology, Nuclear Medicine and Imaging 186
Countries citing papers authored by Matteo Barbieri
This map shows the geographic impact of Matteo Barbieri'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 Matteo Barbieri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matteo Barbieri more than expected).
Fields of papers citing papers by Matteo Barbieri
This network shows the impact of papers produced by Matteo Barbieri. 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 Matteo Barbieri. The network helps show where Matteo Barbieri may publish in the future.
Co-authors
The 25 scholars most cited alongside Matteo Barbieri, 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 | 2025 | 2 | |
| 2 | 2025 | 4 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 2 | |
| 6 | Artificial intelligence for multimodal data integration in oncology Hit paper breakdown → | 2022 | 333 |
| 7 | 2021 | 91 | |
| 8 | 2020 | 16 | |
| 9 | 2020 | 2 | |
| 10 | 2016 | 1 | |
| 11 | 2016 | 1 | |
| 12 | 2015 | 3 | |
| 13 | 2013 | 27 | |
| 14 | 2013 | 10 | |
| 15 | 2006 | 133 | |
| 16 | 2005 | 94 | |
| 17 | Trasformazione genetica del melo Gala con un gene di resistenza a ticchiolatura | 2004 | 1 |
| 18 | 2003 | 20 | |
| 19 | 2002 | 5 | |
| 20 | 1974 | 2 |
About Matteo Barbieri
Matteo Barbieri is a scholar working on Health Informatics, Structural Biology, Biophysics, Control and Systems Engineering and Management, Monitoring, Policy and Law, having authored 33 papers that have together received 941 indexed citations. Recurring topics across this work include Fault Detection and Control Systems (6 papers), Plant Physiology and Cultivation Studies (5 papers), Machine Fault Diagnosis Techniques (5 papers), Plant Pathogens and Fungal Diseases (4 papers), Horticultural and Viticultural Research (3 papers), Landslides and related hazards (3 papers), Multi-Agent Systems and Negotiation (2 papers) and Logic, Reasoning, and Knowledge (2 papers). The work is most often cited by research in Health Informatics (68 citations), Management, Monitoring, Policy and Law (161 citations), Horticulture (13 citations), Biophysics (67 citations) and Radiology, Nuclear Medicine and Imaging (186 citations). Matteo Barbieri has collaborated with scholars based in Italy, Germany and France. Frequent co-authors include Luoting Zhuang, Ming Y. Lu, Drew F. K. Williamson, Muhammad Shaban, Bowen Chen, Anurag Vaidya, Tiffany Chen, Chengkuan Chen, Jana Lipková and Richard J. Chen. Their work appears in journals such as Nature Methods, Theoretical and Applied Genetics, Journal of Intelligent Manufacturing, International Journal of Prognostics and Health Management and HortScience.
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.