Pierre Baldi
Impact in
- Endocrine and Autonomic Systems top 0.1%
- Circadian rhythm and melatonin
- Aging top 0.2%
Papers in
-
- Protein Structure and Dynamics 43
- Machine Learning in Bioinformatics 30
- RNA and protein synthesis mechanisms 23
- Gene expression and cancer classification 20
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- Neural Networks and Applications 38
- Co-authors
- Søren Brunak (20 shared papers)Jianlin Cheng (15 shared papers)Arlo Randall (20 shared papers)Laurent Itti (9 shared papers)Anthony D. Long (2 shared papers)Yves Chauvin (17 shared papers)Peter Sadowski (20 shared papers)Kurt Hornik (4 shared papers)
- Journals
- Bioinformatics (34 papers)Journal of Chemical Information and Modeling (24 papers)Proceedings of the National Academy of Sciences (11 papers)Physical review. D (10 papers)Neural Networks (9 papers)
- Partner nations
- United StatesItalyFrance
In The Last Decade
Pierre Baldi
435 papers receiving 35.8k citations
Pierre Baldi's Hit Papers
Peers
Comparison fields: 5 of 231
- Endocrine and Autonomic Systems 2.6k
- Aging 669
- Molecular Biology 15.3k
- Computational Theory and Mathematics 3.0k
- Artificial Intelligence 5.8k
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-authors
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
Showing the 20 most-cited of 454 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Assessing the accuracy of prediction algorithms for classification: an overview Hit paper breakdown → | 2000 | 1714 |
| 2 | A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes Hit paper breakdown → | 2001 | 1326 |
| 3 | Neural networks and principal component analysis: Learning from examples without local minima Hit paper breakdown → | 1989 | 939 |
| 4 | Bayesian surprise attracts human attention Hit paper breakdown → | 2008 | 894 |
| 5 | SCRATCH: a protein structure and structural feature prediction server Hit paper breakdown → | 2005 | 859 |
| 6 | Prediction of protein stability changes for single‐site mutations using support vector machines Hit paper breakdown → | 2005 | 843 |
| 7 | Autoencoders, Unsupervised Learning, and Deep Architectures Hit paper breakdown → | 2011 | 794 |
| 8 | Bioinformatics the machine learning approach Hit paper breakdown → | 1998 | 730 |
| 9 | Searching for exotic particles in high-energy physics with deep learning Hit paper breakdown → | 2014 | 703 |
| 10 | Mitochondrial mutations in cancer Hit paper breakdown → | 2006 | 685 |
| 11 | Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles Hit paper breakdown → | 2002 | 600 |
| 12 | Reprogramming of the Circadian Clock by Nutritional Challenge Hit paper breakdown → | 2013 | 567 |
| 13 | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy Hit paper breakdown → | 2018 | 539 |
| 14 | SOLpro: accurate sequence-based prediction of protein solubility Hit paper breakdown → | 2009 | 511 |
| 15 | 2006 | 499 | |
| 16 | Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules Hit paper breakdown → | 2013 | 433 |
| 17 | High-throughput prediction of protein antigenicity using protein microarray data Hit paper breakdown → | 2010 | 412 |
| 18 | 1999 | 388 | |
| 19 | 1994 | 365 | |
| 20 | Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas Hit paper breakdown → | 2018 | 360 |
About Pierre Baldi
Pierre Baldi is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Endocrine and Autonomic Systems and Cognitive Neuroscience, having authored 454 papers that have together received 37.2k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (43 papers), Computational Drug Discovery Methods (42 papers), Neural Networks and Applications (38 papers), Circadian rhythm and melatonin (35 papers), Machine Learning in Bioinformatics (30 papers), RNA and protein synthesis mechanisms (23 papers), Gene expression and cancer classification (20 papers) and Neural dynamics and brain function (20 papers). The work is most often cited by research in Endocrine and Autonomic Systems (2.6k citations), Aging (669 citations), Molecular Biology (15.3k citations), Computational Theory and Mathematics (3.0k citations) and Artificial Intelligence (5.8k citations). Pierre Baldi has collaborated with scholars based in United States, Italy and France. Frequent co-authors include Søren Brunak, Jianlin Cheng, Arlo Randall, 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.