Gabriele Corso
Impact in
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- Computational Drug Discovery Methods
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- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
Papers in
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- Protein Structure and Dynamics 2
- Bioinformatics and Genomic Networks 1
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- Advanced Graph Neural Networks 2
- Domain Adaptation and Few-Shot Learning 1
- Bayesian Modeling and Causal Inference 1
- Machine Learning and Data Classification 1
- Co-authors
- Regina Barzilay (5 shared papers)Tommi Jaakkola (4 shared papers)H. Stärk (3 shared papers)Stefanie Jegelka (1 shared paper)Bowen Jing (3 shared papers)Jason Yim (1 shared paper)Petar Veličković (1 shared paper)Dominique Beaini (1 shared paper)
- Journals
- Biophysical Journal (1 paper)Nature Microbiology (1 paper)Nature Reviews Methods Primers (1 paper)Wiley Interdisciplinary Reviews Computational Molecular Science (1 paper)PubMed (1 paper)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Gabriele Corso
6 papers receiving 208 citations
Gabriele Corso's Hit Papers
Peers
Comparison fields: 5 of 79
- Computational Theory and Mathematics 41
- Artificial Intelligence 60
- Health Informatics 2
- Computer Vision and Pattern Recognition 21
- Statistical and Nonlinear Physics 11
Countries citing papers authored by Gabriele Corso
This map shows the geographic impact of Gabriele Corso'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 Gabriele Corso with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriele Corso more than expected).
Fields of papers citing papers by Gabriele Corso
This network shows the impact of papers produced by Gabriele Corso. 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 Gabriele Corso. The network helps show where Gabriele Corso may publish in the future.
Co-authors
The 18 scholars most cited alongside Gabriele Corso, 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 | Graph neural networks Hit paper breakdown → | 2024 | 148 |
| 2 | 2024 | 33 | |
| 3 | Principal Neighbourhood Aggregation for Graph Nets | 2020 | 14 |
| 4 | 2023 | 10 | |
| 5 | 2025 | 3 | |
| 6 | 2024 | 3 |
About Gabriele Corso
Gabriele Corso is a scholar working on Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Clinical Biochemistry and Computational Theory and Mathematics, having authored 6 papers that have together received 211 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (2 papers), Advanced Graph Neural Networks (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Cancer Genomics and Diagnostics (1 paper), Bayesian Modeling and Causal Inference (1 paper), Biosimilars and Bioanalytical Methods (1 paper), Bioinformatics and Genomic Networks (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Computational Theory and Mathematics (41 citations), Artificial Intelligence (60 citations), Health Informatics (2 citations), Computer Vision and Pattern Recognition (21 citations) and Statistical and Nonlinear Physics (11 citations). Gabriele Corso has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Regina Barzilay, Tommi Jaakkola, H. Stärk, Stefanie Jegelka, Bowen Jing, Jason Yim, Petar Veličković, Dominique Beaini, Píetro Lió and Eric D. Brown. Their work appears in journals such as Biophysical Journal, Nature Microbiology, Nature Reviews Methods Primers, Wiley Interdisciplinary Reviews Computational Molecular Science and PubMed.
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.