Maria Luisa Barretta
- Cell Biology top 10%
- Microtubule and mitosis dynamics 7
- Cellular transport and secretion 6
-
- Radiomics and Machine Learning in Medical Imaging 5
- MRI in cancer diagnosis 5
- Hepatology top 10%
- Hepatocellular Carcinoma Treatment and Prognosis 3
-
- AI in cancer detection 3
-
- Retinal Development and Disorders 3
-
- Cholangiocarcinoma and Gallbladder Cancer Studies 3
- Co-authors
- Antonino ColanziRomina Inès CervigniDaniela CordaLetizia CapassoFrancesco RaimondiPasquale SantoroMaria Vittoria BaroneR Paludetto
- Partner nations
- ItalyUnited StatesUnited Kingdom
In The Last Decade
Maria Luisa Barretta
25 papers receiving 700 citations
Peers
Comparison fields: 5 of 82
- Cell Biology 171
- Health Informatics 10
- Radiology, Nuclear Medicine and Imaging 158
- Hepatology 53
- Oncology 179
Countries citing papers authored by Maria Luisa Barretta
This map shows the geographic impact of Maria Luisa Barretta'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 Maria Luisa Barretta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Luisa Barretta more than expected).
Fields of papers citing papers by Maria Luisa Barretta
This network shows the impact of papers produced by Maria Luisa Barretta. 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 Maria Luisa Barretta. The network helps show where Maria Luisa Barretta may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Maria Luisa Barretta, 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 | 2023 | 3 | |
| 2 | 2022 | 6 | |
| 3 | 2022 | 3 | |
| 4 | 2022 | 47 | |
| 5 | 2022 | 8 | |
| 6 | 2021 | 27 | |
| 7 | 2021 | 15 | |
| 8 | 2021 | 46 | |
| 9 | 2021 | 31 | |
| 10 | 2020 | 12 | |
| 11 | 2016 | 33 | |
| 12 | 2015 | 50 | |
| 13 | 2012 | 45 | |
| 14 | 2012 | 8 | |
| 15 | 2011 | 11 | |
| 16 | 2011 | 29 | |
| 17 | 2010 | 37 | |
| 18 | 2009 | 29 | |
| 19 | 2008 | 228 | |
| 20 | 2008 | 3 |
About Maria Luisa Barretta
Maria Luisa Barretta is a scholar working on Otorhinolaryngology, Cell Biology and Radiology, Nuclear Medicine and Imaging, having authored 26 papers that have together received 707 indexed citations. Recurring topics across this work include Microtubule and mitosis dynamics (7 papers), Cellular transport and secretion (6 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), MRI in cancer diagnosis (5 papers), Hepatocellular Carcinoma Treatment and Prognosis (3 papers), AI in cancer detection (3 papers), Retinal Development and Disorders (3 papers) and Cholangiocarcinoma and Gallbladder Cancer Studies (3 papers). The work is most often cited by research in Cell Biology (171 citations), Health Informatics (10 citations) and Radiology, Nuclear Medicine and Imaging (158 citations). Maria Luisa Barretta has collaborated with scholars based in Italy, United States and United Kingdom. Frequent co-authors include Antonino Colanzi, Romina Inès Cervigni, Daniela Corda, Letizia Capasso, Francesco Raimondi, Pasquale Santoro, Maria Vittoria Barone, R Paludetto, Merlin Nanayakkara and Vincenza Granata. Their work appears in journals such as Nature Communications, Journal of Cell Science and FEBS Letters.
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.