Karim Beguir

950 total citations · 1 hit paper
11 papers, 179 citations indexed

About

Karim Beguir is a scholar working on Molecular Biology, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Karim Beguir has authored 11 papers receiving a total of 179 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 2 papers in Control and Systems Engineering and 2 papers in Artificial Intelligence. Recurrent topics in Karim Beguir's work include Genomics and Phylogenetic Studies (3 papers), vaccines and immunoinformatics approaches (2 papers) and Machine Learning in Bioinformatics (2 papers). Karim Beguir is often cited by papers focused on Genomics and Phylogenetic Studies (3 papers), vaccines and immunoinformatics approaches (2 papers) and Machine Learning in Bioinformatics (2 papers). Karim Beguir collaborates with scholars based in Germany, United Kingdom and United States. Karim Beguir's co-authors include Thomas Pierrot, Nicolás López Carranza, Marcin J. Skwark, Guillaume Richard, Hugo Dalla-Torre, Javier Mendoza‐Revilla, Bernardo P. de Almeida, Evan Trop, Christian Dallago and Hassan Sirelkhatim and has published in prestigious journals such as Nature Biotechnology, Nature Methods and Scientific Reports.

In The Last Decade

Karim Beguir

9 papers receiving 177 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Nucleotide Transformer: building and evaluating robust foundation models for human genomics

2024 95 citations Hugo Dalla-Torre, Javier Mendoza‐Revilla et al. Nature Methods profile →

Peers

Karim Beguir

MB

AI

PS

GENET

ID

Thomas Pierrot United Kingdom

Nicolás López Carranza United Kingdom

Favorisen Rosyking Lumbanraja Indonesia

Vinhthuy Phan United States

Shitij Bhargava United States

Claudine Devauchelle Germany

N.A. Shankaracharya India

Mary Ann Tuli United Kingdom

Mingyue Guo China

Brian Kang United States

Thomas Pierrot United Kingdom

Karim Beguir

93 ×1.0

MB

33 ×1.0

AI

19 ×1.0

PS

17 ×1.0

GENET

14 ×1.0

ID

Citations per year, relative to Karim Beguir Karim Beguir (= 1×) peers Thomas Pierrot

Countries citing papers authored by Karim Beguir

Since Specialization

Citations

This map shows the geographic impact of Karim Beguir'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 Karim Beguir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karim Beguir more than expected).

Fields of papers citing papers by Karim Beguir

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Karim Beguir. 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 Karim Beguir. The network helps show where Karim Beguir may publish in the future.

Co-authorship network of co-authors of Karim Beguir

This figure shows the co-authorship network connecting the top 25 collaborators of Karim Beguir. A scholar is included among the top collaborators of Karim Beguir 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 Karim Beguir. Karim Beguir is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Rivera‐de‐Torre, Esperanza, Jakob Berg Jespersen, Wesley Williams, et al.. (2025). InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments. Nature Machine Intelligence. 7(4). 565–579. 7 indexed citations

2.

Almeida, Bernardo P. de, Hugo Dalla-Torre, Guillaume Richard, et al.. (2025). Annotating the genome at single-nucleotide resolution with DNA foundation models. Nature Methods. 22(11). 2301–2315.

3.

Rayment, Ivan, Cécile Petit, Maren Lang, et al.. (2025). Assessing data size requirements for training generalizable sequence-based TCR specificity models via pan-allelic MHC-I point-mutation ligandome evaluation. Scientific Reports. 15(1). 42384–42384.

4.

Almeida, Bernardo P. de, Guillaume Richard, Hugo Dalla-Torre, et al.. (2025). A multimodal conversational agent for DNA, RNA and protein tasks. Nature Machine Intelligence. 7(6). 928–941. 7 indexed citations

5.

Mendoza‐Revilla, Javier, Evan Trop, Maša Roller, et al.. (2024). A foundational large language model for edible plant genomes. Communications Biology. 7(1). 835–835. 34 indexed citations

6.

Dalla-Torre, Hugo, Javier Mendoza‐Revilla, Nicolás López Carranza, et al.. (2024). Nucleotide Transformer: building and evaluating robust foundation models for human genomics. Nature Methods. 22(2). 287–297. 95 indexed citations breakdown →

7.

Beguir, Karim, Marcin J. Skwark, Yunguan Fu, et al.. (2023). Early computational detection of potential high-risk SARS-CoV-2 variants. Computers in Biology and Medicine. 155. 106618–106618. 19 indexed citations

8.

Jenkins, Timothy P., et al.. (2022). Can hackathons unlock a new talent pool from the developing world?. Nature Biotechnology. 40(8). 1297–1298. 1 indexed citations

9.

Pierrot, Thomas, Guillaume Richard, Karim Beguir, & Antoine Cully. (2022). Multi-objective quality diversity optimization. Proceedings of the Genetic and Evolutionary Computation Conference. 139–147. 13 indexed citations

10.

Pierrot, Thomas, et al.. (2021). Sample efficient Quality Diversity for neural continuous control. 1 indexed citations

11.

Pierrot, Thomas, et al.. (2019). Learning Compositional Neural Programs with Recursive Tree Search and Planning. HAL (Le Centre pour la Communication Scientifique Directe). 2 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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