Domenico Pomarico
- Artificial Intelligence top 5%
- Atomic and Molecular Physics, and Optics
- Radiology, Nuclear Medicine and Imaging
- Cancer Research
- Oncology
- Co-authors
- Francesco V. PepePaolo FacchiSaverio PascazioTommaso TufarelliAnnarita FanizziDaniele La ForgiaRaffaella MassafraVittorio Didonna
- Topics
- Quantum Information and Cryptography (8 papers)Breast Cancer Treatment Studies (6 papers)Radiomics and Machine Learning in Medical Imaging (5 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEScientific Reports
- Partner nations
- ItalyAustraliaUnited Kingdom
In The Last Decade
Domenico Pomarico
18 papers receiving 296 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 217
- Atomic and Molecular Physics, and Optics 137
- Radiology, Nuclear Medicine and Imaging 100
- Cancer Research 32
- Oncology 27
Countries citing papers authored by Domenico Pomarico
This map shows the geographic impact of Domenico Pomarico'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 Domenico Pomarico with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Domenico Pomarico more than expected).
Fields of papers citing papers by Domenico Pomarico
This network shows the impact of papers produced by Domenico Pomarico. 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 Domenico Pomarico. The network helps show where Domenico Pomarico may publish in the future.
Co-authorship network of co-authors of Domenico Pomarico
This figure shows the co-authorship network connecting the top 25 collaborators of Domenico Pomarico. A scholar is included among the top collaborators of Domenico Pomarico 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 Domenico Pomarico. Domenico Pomarico 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 | 1 | |
| 3 | 0 | |
| 4 | 14 | |
| 5 | 5 | |
| 6 | 3 | |
| 7 | 11 | |
| 8 | 30 | |
| 9 | 26 | |
| 10 | 4 | |
| 11 | 7 | |
| 12 | 6 | |
| 13 | 31 | |
| 14 | 32 | |
| 15 | 13 | |
| 16 | Decision support systems for the prediction of lymph node involvement in early breast cancer. | 2 |
| 17 | Advancement study of CancerMath model as prognostic tools for predicting Sentinel lymph node metastasis in clinically negative T1 breast cancer patients. | 2 |
| 18 | 28 | |
| 19 | 11 | |
| 20 | 80 |
About Domenico Pomarico
Domenico Pomarico is a scholar working on Artificial Intelligence, Cancer Research and Atomic and Molecular Physics, and Optics, having authored 20 papers that have together received 306 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (8 papers), Breast Cancer Treatment Studies (6 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). The work is most often cited by research in Health Informatics (16 citations), Artificial Intelligence (217 citations) and Atomic and Molecular Physics, and Optics (137 citations). Domenico Pomarico has collaborated with scholars based in Italy, Australia and United Kingdom. Frequent co-authors include Francesco V. Pepe, Paolo Facchi, Saverio Pascazio, Tommaso Tufarelli, Annarita Fanizzi, Daniele La Forgia, Raffaella Massafra, Vittorio Didonna, Alfredo Zito and Pasquale Tamborra. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.
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.