Gianvito Pio
Impact in
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Advanced Clustering Algorithms Research
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- Complex Network Analysis Techniques
Papers in
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- Advanced Clustering Algorithms Research 5
- Advanced Graph Neural Networks 5
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- Gene expression and cancer classification 7
- Bioinformatics and Genomic Networks 6
- RNA Research and Splicing 5
- Gene Regulatory Network Analysis 5
- Co-authors
- Michelangelo Ceci (33 shared papers)Donato Malerba (11 shared papers)Domenica D’Elia (9 shared papers)Nicoletta Del Buono (1 shared paper)Roberto Corizzo (3 shared papers)Massimo Bilancia (2 shared papers)Sašo Džeroski (3 shared papers)Nathalie Japkowicz (2 shared papers)
- Journals
- BMC Bioinformatics (5 papers)Information Sciences (4 papers)Bioinformatics (2 papers)Machine Learning (2 papers)Expert Systems with Applications (2 papers)
- Partner nations
- ItalySloveniaUnited States
In The Last Decade
Gianvito Pio
32 papers receiving 596 citations
Peers
Comparison fields: 5 of 109
- Artificial Intelligence 281
- Statistical and Nonlinear Physics 72
- Information Systems 101
- Cancer Research 65
- Computer Vision and Pattern Recognition 78
Countries citing papers authored by Gianvito Pio
This map shows the geographic impact of Gianvito Pio'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 Gianvito Pio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gianvito Pio more than expected).
Fields of papers citing papers by Gianvito Pio
This network shows the impact of papers produced by Gianvito Pio. 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 Gianvito Pio. The network helps show where Gianvito Pio may publish in the future.
Co-authors
The 25 scholars most cited alongside Gianvito Pio, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 62 | |
| 2 | 2015 | 51 | |
| 3 | 2014 | 43 | |
| 4 | 2019 | 42 | |
| 5 | 2020 | 41 | |
| 6 | 2019 | 36 | |
| 7 | 2017 | 33 | |
| 8 | 2015 | 32 | |
| 9 | 2015 | 30 | |
| 10 | 2013 | 29 | |
| 11 | 2021 | 26 | |
| 12 | 2019 | 25 | |
| 13 | 2020 | 25 | |
| 14 | 2018 | 24 | |
| 15 | 2020 | 23 | |
| 16 | 2022 | 17 | |
| 17 | 2021 | 16 | |
| 18 | 2022 | 9 | |
| 19 | 2021 | 8 | |
| 20 | 2023 | 7 |
About Gianvito Pio
Gianvito Pio is a scholar working on Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics, Cancer Research and Signal Processing, having authored 38 papers that have together received 610 indexed citations. Recurring topics across this work include Gene expression and cancer classification (7 papers), Complex Network Analysis Techniques (6 papers), Bioinformatics and Genomic Networks (6 papers), RNA Research and Splicing (5 papers), Advanced Clustering Algorithms Research (5 papers), Gene Regulatory Network Analysis (5 papers), MicroRNA in disease regulation (5 papers) and Advanced Graph Neural Networks (5 papers). The work is most often cited by research in Artificial Intelligence (281 citations), Statistical and Nonlinear Physics (72 citations), Information Systems (101 citations), Cancer Research (65 citations) and Computer Vision and Pattern Recognition (78 citations). Gianvito Pio has collaborated with scholars based in Italy, Slovenia and United States. Frequent co-authors include Michelangelo Ceci, Donato Malerba, Domenica D’Elia, Nicoletta Del Buono, Roberto Corizzo, Massimo Bilancia, Sašo Džeroski, Nathalie Japkowicz, Vladimir Kuzmanovski and Corrado Appice Annalisa Malerba Donato Loglisci. Their work appears in journals such as BMC Bioinformatics, Information Sciences, Bioinformatics, Machine Learning and Expert Systems with Applications.
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.