Jean‐Marc Steyaert

1.8k total citations · 1 hit paper
42 papers, 1.1k citations indexed

About

Jean‐Marc Steyaert is a scholar working on Molecular Biology, Artificial Intelligence and Cellular and Molecular Neuroscience. According to data from OpenAlex, Jean‐Marc Steyaert has authored 42 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 10 papers in Artificial Intelligence and 7 papers in Cellular and Molecular Neuroscience. Recurrent topics in Jean‐Marc Steyaert's work include RNA and protein synthesis mechanisms (11 papers), Machine Learning in Bioinformatics (9 papers) and Protein Structure and Dynamics (9 papers). Jean‐Marc Steyaert is often cited by papers focused on RNA and protein synthesis mechanisms (11 papers), Machine Learning in Bioinformatics (9 papers) and Protein Structure and Dynamics (9 papers). Jean‐Marc Steyaert collaborates with scholars based in France, United States and Egypt. Jean‐Marc Steyaert's co-authors include Philippe Icard, Hubert Lincet, Marco Alifano, Diana Farhat, S. Shulman, Laurent Schwartz, Marcel Levy Nogueira, Philippe Flajolet, Peter Clote and Jorgelindo da Veiga Moreira and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Nature Chemical Biology.

In The Last Decade

Jean‐Marc Steyaert

41 papers receiving 1.1k citations

Hit Papers

How the Warburg effect supports aggressiveness and drug r... 2018 2026 2020 2023 2018 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jean‐Marc Steyaert France 16 714 396 162 86 77 42 1.1k
Jae Ho Seo United States 20 857 1.2× 413 1.0× 130 0.8× 97 1.1× 70 0.9× 29 1.2k
Zhenming Gao China 21 696 1.0× 270 0.7× 142 0.9× 146 1.7× 48 0.6× 53 1.2k
Monica Nicolau United States 15 862 1.2× 250 0.6× 305 1.9× 97 1.1× 60 0.8× 24 1.5k
Mark E. Schurdak United States 25 1.0k 1.4× 443 1.1× 182 1.1× 56 0.7× 74 1.0× 48 1.7k
Maria Elena Pisanu Italy 20 904 1.3× 603 1.5× 300 1.9× 125 1.5× 70 0.9× 42 1.4k
Sean T. Bailey United States 11 756 1.1× 247 0.6× 191 1.2× 151 1.8× 76 1.0× 14 1.1k
Livnat Jerby Israel 11 1.4k 2.0× 461 1.2× 139 0.9× 63 0.7× 136 1.8× 12 1.7k
Morten Krogh Sweden 21 723 1.0× 206 0.5× 163 1.0× 48 0.6× 61 0.8× 38 1.3k
Loling Song United States 4 842 1.2× 461 1.2× 290 1.8× 91 1.1× 55 0.7× 5 1.3k
Natalia J. Martinez United States 23 1.7k 2.3× 574 1.4× 217 1.3× 57 0.7× 64 0.8× 46 2.2k

Countries citing papers authored by Jean‐Marc Steyaert

Since Specialization
Citations

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

Fields of papers citing papers by Jean‐Marc Steyaert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jean‐Marc Steyaert. 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‐Marc Steyaert. The network helps show where Jean‐Marc Steyaert may publish in the future.

Co-authorship network of co-authors of Jean‐Marc Steyaert

This figure shows the co-authorship network connecting the top 25 collaborators of Jean‐Marc Steyaert. A scholar is included among the top collaborators of Jean‐Marc Steyaert 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‐Marc Steyaert. Jean‐Marc Steyaert 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.
Steyaert, Jean‐Marc, et al.. (2020). A Nested 2-Level Cross-Validation Ensemble Learning Pipeline Suggests a Negative Pressure Against Crosstalk snoRNA-mRNA Interactions in Saccharomyces cerevisiae. Journal of Computational Biology. 27(3). 390–402. 1 indexed citations
2.
Dian, Cyril, Sarah Ciccone, Willy V. Bienvenut, et al.. (2018). Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern. Nature Chemical Biology. 14(7). 671–679. 72 indexed citations
3.
Nogueira, Marcel Levy, et al.. (2017). Mechanical stress increases brain amyloid β, tau, and α‐synuclein concentrations in wild‐type mice. Alzheimer s & Dementia. 14(4). 444–453. 26 indexed citations
4.
Paulevé, Loïc, et al.. (2016). Necessary and sufficient conditions for protocell growth. Journal of Mathematical Biology. 73(6-7). 1627–1664. 2 indexed citations
5.
Steyaert, Jean‐Marc, et al.. (2015). Chemical Schemes for Maintaining Different Compositions Across a Semi-permeable Membrane with Application to Proto-cells. Origins of Life and Evolution of Biospheres. 45(4). 439–454. 2 indexed citations
6.
Gelu‐Siméon, Moana, Maria A. Ostos, Faroudy Boufassa, et al.. (2015). MELD Score Kinetics in Decompensated HIV+/HCV+ Patients. Medicine. 94(30). e1239–e1239. 1 indexed citations
7.
Nogueira, Marcel Levy, et al.. (2015). Mechanical Stress as the Common Denominator between Chronic Inflammation, Cancer, and Alzheimer’s Disease. Frontiers in Oncology. 5. 197–197. 21 indexed citations
8.
Icard, Philippe, et al.. (2014). The metabolic cooperation between cells in solid cancer tumors. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1846(1). 216–225. 57 indexed citations
9.
Tran, Van Du T., et al.. (2012). A graph-theoretic approach for classification and structure prediction of transmembrane β-barrel proteins. BMC Genomics. 13(Suppl 2). S5–S5. 14 indexed citations
10.
Régnier, Mireille, et al.. (2011). Counting RNA Pseudoknotted Structures. Journal of Computational Biology. 18(10). 1339–1351. 5 indexed citations
11.
Clote, Peter, Yann Ponty, & Jean‐Marc Steyaert. (2011). Expected distance between terminal nucleotides of RNA secondary structures. Journal of Mathematical Biology. 65(3). 581–599. 23 indexed citations
12.
Tran, Van Du T., et al.. (2011). Energy-based classification and structure prediction of transmembrane beta-barrel proteins. 30. 159–164. 2 indexed citations
13.
Tran, Van Du T., et al.. (2011). Prediction of permuted super-secondary structures in β-barrel proteins. 110–111. 2 indexed citations
14.
Tran, Van Du T., et al.. (2009). Prediction of super-secondary structure in α-helical and β-barrel transmembrane proteins. BMC Bioinformatics. 10(S13). 2 indexed citations
15.
Schwartz, Laurent, Mohammad Abolhassani, Jean‐Marc Steyaert, et al.. (2008). Hyperosmotic stress contributes to mouse colonic inflammation through the methylation of protein phosphatase 2A. American Journal of Physiology-Gastrointestinal and Liver Physiology. 295(5). G934–G941. 39 indexed citations
16.
Waldispühl, Jérôme, Bonnie Berger, Peter Clote, & Jean‐Marc Steyaert. (2006). transFold: a web server for predicting the structure and residue contacts of transmembrane beta-barrels. Nucleic Acids Research. 34(Web Server). W189–W193. 25 indexed citations
17.
Clote, Peter, et al.. (2005). Energy landscape of k-point mutants of an RNA molecule. Bioinformatics. 21(22). 4140–4147. 14 indexed citations
18.
Steyaert, Jean‐Marc, et al.. (2004). An improved algorithm for generalized comparison of minisatellites. Journal of Discrete Algorithms. 3(2-4). 375–389. 2 indexed citations
19.
Steyaert, Jean‐Marc, et al.. (1990). Algebraic simplification in computer algebra: an analysis of bottom-up algorithms. Theoretical Computer Science. 74(3). 273–298. 3 indexed citations
20.
Flajolet, Philippe & Jean‐Marc Steyaert. (1973). Decision Problems for Multihead Finite Automata.. 2(3). 225–230. 8 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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