Gabriella Minchiotti
- Molecular Biology top 5%
- Pluripotent Stem Cells Research 25
- Developmental Biology and Gene Regulation 16
- Congenital heart defects research 13
- Muscle Physiology and Disorders 11
- CRISPR and Genetic Engineering 11
- RNA Research and Splicing 10
- Genomics and Chromatin Dynamics 7
- Cancer Research top 5%
- Cell Biology top 5%
- Developmental Neuroscience top 10%
- Oncology top 10%
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- 3D Printing in Biomedical Research 8
- Co-authors
- Eduardo J. PatriarcaCristina D’AnielloM. Graziella PersicoSilvia ParisiOmbretta GuardiolaPier Paolo Di NoceraFederica CermolaAnnalisa Fico
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Nucleic Acids Research (1 paper)Journal of Biological Chemistry (1 paper)
- Partner nations
- ItalyUnited StatesFrance
In The Last Decade
Gabriella Minchiotti
81 papers receiving 3.0k citations
Peers
Comparison fields: 5 of 124
- Molecular Biology 2.3k
- Cancer Research 378
- Cell Biology 328
- Developmental Neuroscience 64
- Oncology 337
Countries citing papers authored by Gabriella Minchiotti
This map shows the geographic impact of Gabriella Minchiotti'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 Gabriella Minchiotti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriella Minchiotti more than expected).
Fields of papers citing papers by Gabriella Minchiotti
This network shows the impact of papers produced by Gabriella Minchiotti. 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 Gabriella Minchiotti. The network helps show where Gabriella Minchiotti may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gabriella Minchiotti, 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 | 2024 | 12 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 2 | |
| 4 | 2022 | 3 | |
| 5 | 2022 | 2 | |
| 6 | 2022 | 10 | |
| 7 | 2021 | 24 | |
| 8 | 2020 | 115 | |
| 9 | 2019 | 43 | |
| 10 | 2019 | 65 | |
| 11 | 2019 | 21 | |
| 12 | 2017 | 48 | |
| 13 | 2015 | 47 | |
| 14 | 2011 | 26 | |
| 15 | 2011 | 11 | |
| 16 | 2009 | 80 | |
| 17 | 2009 | 47 | |
| 18 | A versatile method for differentiation of multiple neuronal subtypes from mouse embryonic stem cells | 2006 | 5 |
| 19 | 2000 | 103 | |
| 20 | 1997 | 1 |
About Gabriella Minchiotti
Gabriella Minchiotti is a scholar working on Molecular Biology, Cancer Research and Aging, having authored 81 papers that have together received 3.0k indexed citations. Recurring topics across this work include Pluripotent Stem Cells Research (25 papers), Developmental Biology and Gene Regulation (16 papers), Congenital heart defects research (13 papers), Muscle Physiology and Disorders (11 papers), CRISPR and Genetic Engineering (11 papers), RNA Research and Splicing (10 papers), 3D Printing in Biomedical Research (8 papers) and Genomics and Chromatin Dynamics (7 papers). The work is most often cited by research in Molecular Biology (2.3k citations), Cancer Research (378 citations) and Cell Biology (328 citations). Gabriella Minchiotti has collaborated with scholars based in Italy, United States and France. Frequent co-authors include Eduardo J. Patriarca, Cristina D’Aniello, M. Graziella Persico, Silvia Parisi, Ombretta Guardiola, Pier Paolo Di Nocera, Federica Cermola, Annalisa Fico, Eileen D. Adamson and Dario De Cesare. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.
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.