Pierre Baldi
- Aging top 0.2%
- Endocrine and Autonomic Systems top 0.1%
- Circadian rhythm and melatonin 35
- Molecular Biology top 0.1%
- Protein Structure and Dynamics 45
- Machine Learning in Bioinformatics 37
- RNA and protein synthesis mechanisms 29
- Gene expression and cancer classification 21
- Computational Theory and Mathematics top 0.05%
- Computational Drug Discovery Methods 39
- Health Informatics top 0.5%
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- Neural Networks and Applications 38
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- Machine Learning in Materials Science 19
- Co-authors
- Jianlin ChengArlo RandallSøren BrunakLaurent IttiAnthony D. LongYves ChauvinPeter SadowskiKurt Hornik
- Journals
- Bioinformatics (34 papers)Journal of Chemical Information and Modeling (24 papers)Proceedings of the National Academy of Sciences (11 papers)
- Partner nations
- United StatesItalyFrance
In The Last Decade
Pierre Baldi
414 papers receiving 33.0k citations
Hit Papers
Peers
Comparison fields: 5 of 232
- Aging 697
- Endocrine and Autonomic Systems 2.6k
- Molecular Biology 15.3k
- Computational Theory and Mathematics 2.8k
- Health Informatics 233
Countries citing papers authored by Pierre Baldi
This map shows the geographic impact of Pierre Baldi'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 Pierre Baldi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pierre Baldi more than expected).
Fields of papers citing papers by Pierre Baldi
This network shows the impact of papers produced by Pierre Baldi. 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 Pierre Baldi. The network helps show where Pierre Baldi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pierre Baldi, 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 | 2025 | 0 | |
| 2 | 2023 | 1 | |
| 3 | 2023 | 5 | |
| 4 | 2022 | 26 | |
| 5 | 2022 | 4 | |
| 6 | 2021 | 11 | |
| 7 | 2021 | 23 | |
| 8 | 2021 | 8 | |
| 9 | 2020 | 1 | |
| 10 | 2020 | 51 | |
| 11 | 2020 | 14 | |
| 12 | 2013 | 205 | |
| 13 | Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction | 2012 | 26 |
| 14 | Autoencoders, Unsupervised Learning, and Deep Architecturesbreakdown → | 2011 | 728 |
| 15 | 2011 | 101 | |
| 16 | Mining Internet-Scale Software Repositories | 2007 | 22 |
| 17 | 2006 | 168 | |
| 18 | U-MAC: a proactive and adaptive UWB medium access control protocol: Research Articles | 2005 | 1 |
| 19 | 2003 | 138 | |
| 20 | Universal Approximation and Learning of Trajectories Using Oscillators | 1995 | 4 |
About Pierre Baldi
Pierre Baldi is a scholar working on Aging, Endocrine and Autonomic Systems and Health Informatics, having authored 428 papers that have together received 34.1k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (45 papers), Computational Drug Discovery Methods (39 papers), Neural Networks and Applications (38 papers), Machine Learning in Bioinformatics (37 papers), Circadian rhythm and melatonin (35 papers), RNA and protein synthesis mechanisms (29 papers), Gene expression and cancer classification (21 papers) and Machine Learning in Materials Science (19 papers). The work is most often cited by research in Aging (697 citations), Endocrine and Autonomic Systems (2.6k citations) and Molecular Biology (15.3k citations). Pierre Baldi has collaborated with scholars based in United States, Italy and France. Frequent co-authors include Jianlin Cheng, Arlo Randall, Søren Brunak, Laurent Itti, Anthony D. Long, Yves Chauvin, Peter Sadowski, Kurt Hornik, Gianluca Pollastri and Michael J. Sweredoski. Their work appears in journals such as Bioinformatics, Journal of Chemical Information and Modeling, Proceedings of the National Academy of Sciences, Physical review. D and Neural Networks.
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.