Sergio Bacallado

1.9k total citations
19 papers, 353 citations indexed

About

Sergio Bacallado is a scholar working on Molecular Biology, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Sergio Bacallado has authored 19 papers receiving a total of 353 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 8 papers in Artificial Intelligence and 8 papers in Statistics and Probability. Recurrent topics in Sergio Bacallado's work include Bayesian Methods and Mixture Models (8 papers), Protein Structure and Dynamics (6 papers) and Statistical Methods and Bayesian Inference (4 papers). Sergio Bacallado is often cited by papers focused on Bayesian Methods and Mixture Models (8 papers), Protein Structure and Dynamics (6 papers) and Statistical Methods and Bayesian Inference (4 papers). Sergio Bacallado collaborates with scholars based in United States, United Kingdom and Italy. Sergio Bacallado's co-authors include Vijay S. Pande, Xuhui Huang, Gregory R. Bowman, John D. Chodera, José Miguel Hernández-Lobato, Andreas Bender, Gregor N. C. Simm, Lorenzo Trippa, Stefano Favaro and Susan Holmes and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and The Journal of Chemical Physics.

In The Last Decade

Sergio Bacallado

17 papers receiving 348 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sergio Bacallado United States	8	228	70	68	58	42	19	353
Wanda Niemyska Poland	11	358 1.6×	103 1.5×	64 0.9×	31 0.5×	88 2.1×	23	509
Valerio Rizzi Switzerland	13	280 1.2×	198 2.8×	86 1.3×	59 1.0×	153 3.6×	27	520
Ge Yunhui United States	12	269 1.2×	79 1.1×	76 1.1×	67 1.2×	36 0.9×	29	361
David Allouche France	10	223 1.0×	69 1.0×	49 0.7×	25 0.4×	72 1.7×	16	380
Konstantin Fackeldey Germany	10	388 1.7×	118 1.7×	301 4.4×	21 0.4×	22 0.5×	33	623
Ralf Banisch Germany	8	119 0.5×	66 0.9×	28 0.4×	26 0.4×	38 0.9×	14	243
Benjamin J. Killian United States	8	316 1.4×	109 1.6×	99 1.5×	68 1.2×	99 2.4×	12	423
Ryan L. Hayes United States	15	810 3.6×	134 1.9×	127 1.9×	69 1.2×	84 2.0×	29	912
Bernd N. M. van Buuren Sweden	8	537 2.4×	82 1.2×	57 0.8×	103 1.8×	10 0.2×	11	714

Countries citing papers authored by Sergio Bacallado

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Sergio Bacallado

This figure shows the co-authorship network connecting the top 25 collaborators of Sergio Bacallado. A scholar is included among the top collaborators of Sergio Bacallado based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sergio Bacallado. Sergio Bacallado is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Mohamed, Ahmed M., Chenhao Li, Tommi Vatanen, et al.. (2025). Protein language models uncover carbohydrate-active enzyme function in metagenomics. BMC Bioinformatics. 26(1). 285–285.

Seal, Srijit, Emily J. Geddes, Collette S. Guy, et al.. (2025). Transfer learning enables discovery of sub-micromolar antibacterials for ESKAPE pathogens from ultra-large chemical spaces. Chemical Science. 16(45). 21518–21533.

Seal, Srijit, et al.. (2024). Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization. Journal of Cheminformatics. 16(1). 115–115. 2 indexed citations

Simm, Gregor N. C., et al.. (2022). DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. Journal of Chemical Information and Modeling. 62(15). 3486–3502. 60 indexed citations

Ventz, Steffen, Sergio Bacallado, Rifaquat Rahman, et al.. (2021). The effects of releasing early results from ongoing clinical trials. Nature Communications. 12(1). 801–801. 6 indexed citations

Ramakrishnan, Nisha K., Stephen Thompson, David J. Williamson, et al.. (2021). Preclinical evaluation of (S)-[18F]GE387, a novel 18-kDa translocator protein (TSPO) PET radioligand with low binding sensitivity to human polymorphism rs6971. European Journal of Nuclear Medicine and Molecular Imaging. 49(1). 125–136. 16 indexed citations

Zhao, Qingyuan, et al.. (2021). BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic. The Annals of Applied Statistics. 15(1). 16 indexed citations

Bacallado, Sergio, et al.. (2021). Perfect Sampling of the Posterior in the Hierarchical Pitman–Yor Process. Bayesian Analysis. 17(3). 685–709. 3 indexed citations

Ventz, Steffen, Matteo Cellamare, Sergio Bacallado, & Lorenzo Trippa. (2018). Bayesian Uncertainty Directed Trial Designs. Journal of the American Statistical Association. 114(527). 962–974. 6 indexed citations

10.

Bacallado, Sergio, et al.. (2017). Sufficientness Postulates for Gibbs-Type Priors and Hierarchical Generalizations. Statistical Science. 32(4). 7 indexed citations

11.

Ren, Boyu, Sergio Bacallado, Stefano Favaro, Susan Holmes, & Lorenzo Trippa. (2017). Bayesian Nonparametric Ordination for the Analysis of Microbial Communities.. Apollo (University of Cambridge). 12 indexed citations

12.

Bacallado, Sergio, Persi Diaconis, & Susan Holmes. (2015). de Finetti Priors using Markov chain Monte Carlo computations. Statistics and Computing. 25(4). 797–808. 7 indexed citations

13.

Bacallado, Sergio, Vijay S. Pande, Stefano Favaro, & Lorenzo Trippa. (2015). Bayesian Regularization of the Length of Memory in Reversible Sequences. Journal of the Royal Statistical Society Series B (Statistical Methodology). 78(4). 933–946. 2 indexed citations

14.

Bacallado, Sergio, Stefano Favaro, & Lorenzo Trippa. (2015). Looking-backward probabilities for Gibbs-type exchangeable random partitions. Bernoulli. 21(1). 3 indexed citations

15.

Bacallado, Sergio, Stefano Favaro, & Lorenzo Trippa. (2014). Bayesian nonparametric inference for shared species richness in multiple populations. Journal of Statistical Planning and Inference. 166. 14–23. 2 indexed citations

16.

Bacallado, Sergio, et al.. (2013). Persistent Topology and Metastable State in Conformational Dynamics. PLoS ONE. 8(4). e58699–e58699. 20 indexed citations

17.

Bacallado, Sergio. (2011). Bayesian analysis of variable-order, reversible Markov chains. The Annals of Statistics. 39(2). 13 indexed citations

18.

Huang, Xuhui, Gregory R. Bowman, Sergio Bacallado, & Vijay S. Pande. (2009). Rapid equilibrium sampling initiated from nonequilibrium data. Proceedings of the National Academy of Sciences. 106(47). 19765–19769. 134 indexed citations

19.

Bacallado, Sergio, John D. Chodera, & Vijay S. Pande. (2009). Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint. The Journal of Chemical Physics. 131(4). 45106–45106. 44 indexed citations

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.

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