Sergio Bacallado
Impact in
-
- Computational Drug Discovery Methods
- Statistics and Probability top 10%
Papers in
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- Protein Structure and Dynamics 6
- Gut microbiota and health 2
- RNA and protein synthesis mechanisms 2
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- Bayesian Methods and Mixture Models 8
- Co-authors
- Vijay S. Pande (4 shared papers)Gregory R. Bowman (1 shared paper)Xuhui Huang (1 shared paper)John D. Chodera (1 shared paper)Andreas Bender (3 shared papers)Gregor N. C. Simm (1 shared paper)José Miguel Hernández-Lobato (1 shared paper)Lorenzo Trippa (8 shared papers)
- Journals
- Nature Communications (1 paper)Bernoulli (1 paper)The Journal of Chemical Physics (1 paper)PLoS ONE (1 paper)European Journal of Nuclear Medicine and Molecular Imaging (1 paper)
- Partner nations
- United StatesUnited KingdomItaly
In The Last Decade
Sergio Bacallado
17 papers receiving 355 citations
Peers
Comparison fields: 5 of 95
- Computational Theory and Mathematics 72
- Statistics and Probability 33
- Spectroscopy 58
- Computational Mathematics 2
- Molecular Biology 224
Countries citing papers authored by Sergio Bacallado
This map shows the geographic impact of Sergio Bacallado'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 Sergio Bacallado with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergio Bacallado more than expected).
Fields of papers citing papers by Sergio Bacallado
This network shows the impact of papers produced by Sergio Bacallado. 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 Sergio Bacallado. The network helps show where Sergio Bacallado may publish in the future.
Co-authors
The 25 scholars most cited alongside Sergio Bacallado, 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 | 2009 | 134 | |
| 2 | 2022 | 67 | |
| 3 | 2009 | 44 | |
| 4 | 2013 | 20 | |
| 5 | 2021 | 16 | |
| 6 | 2021 | 16 | |
| 7 | 2011 | 13 | |
| 8 | 2017 | 12 | |
| 9 | 2017 | 7 | |
| 10 | 2015 | 6 | |
| 11 | 2021 | 6 | |
| 12 | 2018 | 6 | |
| 13 | 2024 | 3 | |
| 14 | 2021 | 3 | |
| 15 | 2015 | 3 | |
| 16 | 2015 | 2 | |
| 17 | 2014 | 2 | |
| 18 | 2025 | 0 | |
| 19 | 2025 | 0 |
About Sergio Bacallado
Sergio Bacallado is a scholar working on Molecular Biology, Artificial Intelligence, Statistics and Probability, Computational Theory and Mathematics and Economics and Econometrics, having authored 19 papers that have together received 360 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (8 papers), Protein Structure and Dynamics (6 papers), Statistical Methods and Bayesian Inference (4 papers), Statistical Methods and Inference (3 papers), Markov Chains and Monte Carlo Methods (2 papers), Gut microbiota and health (2 papers), Computational Drug Discovery Methods (2 papers) and RNA and protein synthesis mechanisms (2 papers). The work is most often cited by research in Computational Theory and Mathematics (72 citations), Statistics and Probability (33 citations), Spectroscopy (58 citations), Computational Mathematics (2 citations) and Molecular Biology (224 citations). Sergio Bacallado has collaborated with scholars based in United States, United Kingdom and Italy. Frequent co-authors include Vijay S. Pande, Gregory R. Bowman, Xuhui Huang, John D. Chodera, Andreas Bender, Gregor N. C. Simm, José Miguel Hernández-Lobato, Lorenzo Trippa, Stefano Favaro and Gunnar Carlsson. Their work appears in journals such as Nature Communications, Bernoulli, The Journal of Chemical Physics, PLoS ONE and European Journal of Nuclear Medicine and Molecular Imaging.
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.