Marco Podda
Impact in
- Biochemistry top 5%
- Antioxidant Activity and Oxidative Stress
- Biochemical Acid Research Studies
- Nutrition and Dietetics top 10%
- Vitamin C and Antioxidants Research
Papers in
-
- Bioinformatics and Genomic Networks 4
- Coenzyme Q10 studies and effects 2
-
- Advanced Graph Neural Networks 8
- Topic Modeling 5
- Co-authors
- Lester Packer (2 shared papers)Davide Bacciu (10 shared papers)Alessio Micheli (12 shared papers)Maret G. Traber (1 shared paper)C. W. Weber (1 shared paper)Federico Errica (4 shared papers)Heinz Ulrich (1 shared paper)Hans Tritschler (1 shared paper)
- Journals
- iScience (1 paper)Scientific Reports (1 paper)Journal of Lipid Research (1 paper)Bioinformatics (1 paper)IEEE Transactions on Emerging Topics in Computing (1 paper)
- Partner nations
- ItalyUnited StatesSwitzerland
In The Last Decade
Marco Podda
16 papers receiving 693 citations
Peers
Comparison fields: 5 of 130
- Biochemistry 183
- Biochemistry 86
- Nutrition and Dietetics 138
- Health Informatics 8
- Artificial Intelligence 178
Countries citing papers authored by Marco Podda
This map shows the geographic impact of Marco Podda'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 Marco Podda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Podda more than expected).
Fields of papers citing papers by Marco Podda
This network shows the impact of papers produced by Marco Podda. 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 Marco Podda. The network helps show where Marco Podda may publish in the future.
Co-authors
The 24 scholars most cited alongside Marco Podda, 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 | 1996 | 293 | |
| 2 | 2020 | 190 | |
| 3 | 1994 | 82 | |
| 4 | 2018 | 61 | |
| 5 | 2019 | 49 | |
| 6 | 2020 | 15 | |
| 7 | 2023 | 5 | |
| 8 | A Deep Generative Model for Fragment-Based Molecule Generation. | 2020 | 4 |
| 9 | Graph generation by sequential edge prediction. | 2019 | 4 |
| 10 | 2024 | 3 | |
| 11 | 2020 | 3 | |
| 12 | 2023 | 2 | |
| 13 | 2020 | 2 | |
| 14 | 2023 | 1 | |
| 15 | 2025 | 1 | |
| 16 | 2021 | 1 | |
| 17 | 2024 | 0 | |
| 18 | 2025 | 0 |
About Marco Podda
Marco Podda is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition and Materials Chemistry, having authored 18 papers that have together received 716 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (8 papers), Computational Drug Discovery Methods (5 papers), Topic Modeling (5 papers), Machine Learning in Materials Science (4 papers), Graph Theory and Algorithms (4 papers), Bioinformatics and Genomic Networks (4 papers), Complex Network Analysis Techniques (2 papers) and Coenzyme Q10 studies and effects (2 papers). The work is most often cited by research in Biochemistry (183 citations), Biochemistry (86 citations), Nutrition and Dietetics (138 citations), Health Informatics (8 citations) and Artificial Intelligence (178 citations). Marco Podda has collaborated with scholars based in Italy, United States and Switzerland. Frequent co-authors include Lester Packer, Davide Bacciu, Alessio Micheli, Maret G. Traber, C. W. Weber, Federico Errica, Heinz Ulrich, Hans Tritschler, Luigi Gagliardi and Roberto Bellù. Their work appears in journals such as iScience, Scientific Reports, Journal of Lipid Research, Bioinformatics and IEEE Transactions on Emerging Topics in Computing.
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.