Marco Podda
Impact in
- Biochemistry top 2%
- 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 5
- Coenzyme Q10 studies and effects 2
-
- Advanced Graph Neural Networks 9
- Topic Modeling 5
- Co-authors
- Lester Packer (2 shared papers)Alessio Micheli (14 shared papers)Davide Bacciu (10 shared papers)C. W. Weber (1 shared paper)Maret G. Traber (1 shared paper)Federico Errica (4 shared papers)Hans Tritschler (1 shared paper)Heinz Ulrich (1 shared paper)
- Journals
- Machine Learning (2 papers)Scientific Reports (1 paper)Neural Networks (1 paper)Biochemical and Biophysical Research Communications (1 paper)Neurocomputing (1 paper)
- Partner nations
- ItalyUnited StatesGermany
In The Last Decade
Marco Podda
17 papers receiving 706 citations
Peers
Comparison fields: 5 of 127
- Biochemistry 176
- Biochemistry 86
- Nutrition and Dietetics 119
- Artificial Intelligence 181
- Health Informatics 6
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 | 195 | |
| 3 | 1994 | 82 | |
| 4 | 2018 | 62 | |
| 5 | 2019 | 50 | |
| 6 | 2020 | 16 | |
| 7 | 2024 | 5 | |
| 8 | 2023 | 5 | |
| 9 | Graph generation by sequential edge prediction. | 2019 | 4 |
| 10 | A Deep Generative Model for Fragment-Based Molecule Generation. | 2020 | 4 |
| 11 | 2020 | 3 | |
| 12 | 2023 | 3 | |
| 13 | 2020 | 3 | |
| 14 | 2023 | 1 | |
| 15 | 2025 | 1 | |
| 16 | 2024 | 1 | |
| 17 | 2021 | 1 | |
| 18 | 2025 | 0 | |
| 19 | 2025 | 0 | |
| 20 | 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 20 papers that have together received 729 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (9 papers), Bioinformatics and Genomic Networks (5 papers), Topic Modeling (5 papers), Computational Drug Discovery Methods (5 papers), Graph Theory and Algorithms (4 papers), Machine Learning in Materials Science (4 papers), Complex Network Analysis Techniques (3 papers) and Coenzyme Q10 studies and effects (2 papers). The work is most often cited by research in Biochemistry (176 citations), Biochemistry (86 citations), Nutrition and Dietetics (119 citations), Artificial Intelligence (181 citations) and Health Informatics (6 citations). Marco Podda has collaborated with scholars based in Italy, United States and Germany. Frequent co-authors include Lester Packer, Alessio Micheli, Davide Bacciu, C. W. Weber, Maret G. Traber, Federico Errica, Hans Tritschler, Heinz Ulrich, Roberto Bellù and Luigi Gagliardi. Their work appears in journals such as Machine Learning, Scientific Reports, Neural Networks, Biochemical and Biophysical Research Communications and Neurocomputing.
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.