Jean-François Gibrat

5.1k total citations · 1 hit paper
34 papers, 2.4k citations indexed

About

Jean-François Gibrat is a scholar working on Molecular Biology, Materials Chemistry and Artificial Intelligence. According to data from OpenAlex, Jean-François Gibrat has authored 34 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Molecular Biology, 9 papers in Materials Chemistry and 4 papers in Artificial Intelligence. Recurrent topics in Jean-François Gibrat's work include Protein Structure and Dynamics (15 papers), Genomics and Phylogenetic Studies (13 papers) and RNA and protein synthesis mechanisms (12 papers). Jean-François Gibrat is often cited by papers focused on Protein Structure and Dynamics (15 papers), Genomics and Phylogenetic Studies (13 papers) and RNA and protein synthesis mechanisms (12 papers). Jean-François Gibrat collaborates with scholars based in France, United States and Morocco. Jean-François Gibrat's co-authors include Barry Robson, Jean Garnier, J. Garnier, Juliette Martin, Jonathan M. Levin, Valérie Biou, Josselin Garnier, Valentin Loux, Antoine Marin and Jean-François Taly and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Bioinformatics.

In The Last Decade

Jean-François Gibrat

33 papers receiving 2.4k citations

Hit Papers

[32] GOR method for predicting protein secondary structur... 1996 2026 2006 2016 1996 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jean-François Gibrat France 13 1.9k 391 305 220 170 34 2.4k
Catherine H. Schein United States 34 2.0k 1.0× 229 0.6× 104 0.3× 479 2.2× 261 1.5× 113 3.7k
Enrique Querol Spain 32 2.5k 1.3× 503 1.3× 58 0.2× 230 1.0× 277 1.6× 155 3.7k
Xiang Gao China 31 1.4k 0.7× 114 0.3× 245 0.8× 282 1.3× 198 1.2× 84 2.4k
Jean Vandenhaute Belgium 28 2.7k 1.4× 81 0.2× 113 0.4× 255 1.2× 171 1.0× 50 3.5k
Zengyi Chang China 31 2.1k 1.1× 777 2.0× 133 0.4× 576 2.6× 190 1.1× 82 2.7k
Jean Garnier France 20 1.9k 1.0× 367 0.9× 46 0.2× 324 1.5× 107 0.6× 41 2.7k
Jaap Heringa Netherlands 33 2.9k 1.5× 417 1.1× 56 0.2× 463 2.1× 215 1.3× 103 3.7k
Christophe Combet France 22 2.0k 1.0× 190 0.5× 61 0.2× 499 2.3× 276 1.6× 37 3.4k
Veronica Canadien Canada 16 2.6k 1.3× 123 0.3× 146 0.5× 505 2.3× 163 1.0× 17 3.6k
Daniel Buchan United Kingdom 16 2.8k 1.4× 424 1.1× 43 0.1× 365 1.7× 240 1.4× 24 3.5k

Countries citing papers authored by Jean-François Gibrat

Since Specialization
Citations

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

Fields of papers citing papers by Jean-François Gibrat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jean-François Gibrat. 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 Jean-François Gibrat. The network helps show where Jean-François Gibrat may publish in the future.

Co-authorship network of co-authors of Jean-François Gibrat

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

All Works

20 of 20 papers shown
1.
Gibrat, Jean-François. (2018). A short note on dynamic programming in a band. BMC Bioinformatics. 19(1). 226–226. 2 indexed citations
2.
Loux, Valentin, et al.. (2014). Insyght: navigating amongst abundant homologues, syntenies and gene functional annotations in bacteria, it's that symbol!. Nucleic Acids Research. 42(21). e162–e162. 4 indexed citations
3.
Schbath, Sophie, et al.. (2012). Mapping Reads on a Genomic Sequence: An Algorithmic Overview and a Practical Comparative Analysis. Journal of Computational Biology. 19(6). 796–813. 53 indexed citations
4.
Launay, Guillaume, et al.. (2012). Automatic modeling of mammalian olfactory receptors and docking of odorants. Protein Engineering Design and Selection. 25(8). 377–386. 52 indexed citations
5.
Launay, Guillaume, et al.. (2012). Modeling of mammalian olfactory receptors and docking of odorants. Biophysical Reviews. 4(3). 255–269. 14 indexed citations
6.
Launay, Guillaume, et al.. (2011). Statistical Significance of Threading Scores. Journal of Computational Biology. 19(1). 13–29. 1 indexed citations
7.
Zimmermann, Karel & Jean-François Gibrat. (2010). Amino acid "little Big Bang": Representing amino acid substitution matrices as dot products of Euclidian vectors. BMC Bioinformatics. 11(1). 4–4. 8 indexed citations
8.
Collet, Guillaume, Rumen Andonov, Nicola Yanev, & Jean-François Gibrat. (2010). Local protein threading by Mixed Integer Programming. Discrete Applied Mathematics. 159(16). 1707–1716. 2 indexed citations
9.
Wessner, Marc, Martin Senger, Franck Samson, et al.. (2008). BioWorkFlow: Web Services toolkit and workflow applications evaluation to deploy a confidence network. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
10.
Azé, Jérôme, Claire Toffano‐Nioche, Valentin Loux, et al.. (2008). Towards a semi-automatic functional annotation tool based on decision-tree techniques. BMC Proceedings. 2(S4). S3–S3. 3 indexed citations
11.
Tai, Chin‐Hsien, et al.. (2008). Towards an automatic classification of protein structural domains based on structural similarity. BMC Bioinformatics. 9(1). 74–74. 14 indexed citations
12.
Tai, Chin‐Hsien, et al.. (2006). ROC and confusion analysis of structure comparison methods identify the main causes of divergence from manual protein classification. BMC Bioinformatics. 7(1). 206–206. 23 indexed citations
13.
Martin, Juliette, et al.. (2005). Protein secondary structure assignment revisited: a detailed analysis of different assignment methods. BMC Structural Biology. 5(1). 17–17. 130 indexed citations
14.
Andonov, Rumen, et al.. (2005). FROST: Revisited and Distributed. 200a–200a. 1 indexed citations
15.
Bryson, Kevin, G. Flucke, Mike Joy, et al.. (2002). From GeneWeaver to Agmial. Warwick Research Archive Portal (University of Warwick). 1 indexed citations
16.
Gibrat, Jean-François, et al.. (1998). In unison: regularization of protein secondary structure predictions that makes use of multiple sequence alignments. Protein Engineering Design and Selection. 11(10). 861–865. 5 indexed citations
17.
Garnier, Jean, Jean-François Gibrat, & Barry Robson. (1996). [32] GOR method for predicting protein secondary structure from amino acid sequence. Methods in enzymology on CD-ROM/Methods in enzymology. 266. 540–553. 1031 indexed citations breakdown →
18.
Gibrat, Jean-François, et al.. (1988). Molecular modeling of two regulatory proteins, fix K and fn R, homologous to CAP. Journal of Molecular Graphics. 6(4). 218–218. 1 indexed citations
19.
Biou, Valérie, Jean-François Gibrat, Jonathan M. Levin, Barry Robson, & Josselin Garnier. (1988). Secondary structure prediction: combination of three different methods. Protein Engineering Design and Selection. 2(3). 185–191. 167 indexed citations
20.
Gibrat, Jean-François, J. Garnier, & Barry Robson. (1987). Further developments of protein secondary structure prediction using information theory. Journal of Molecular Biology. 198(3). 425–443. 452 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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