Jo Maertens

2.0k total citations
48 papers, 1.6k citations indexed

About

Jo Maertens is a scholar working on Molecular Biology, Genetics and Biomedical Engineering. According to data from OpenAlex, Jo Maertens has authored 48 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 12 papers in Genetics and 10 papers in Biomedical Engineering. Recurrent topics in Jo Maertens's work include Microbial Metabolic Engineering and Bioproduction (27 papers), Viral Infectious Diseases and Gene Expression in Insects (13 papers) and RNA and protein synthesis mechanisms (13 papers). Jo Maertens is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (27 papers), Viral Infectious Diseases and Gene Expression in Insects (13 papers) and RNA and protein synthesis mechanisms (13 papers). Jo Maertens collaborates with scholars based in Belgium, Netherlands and Canada. Jo Maertens's co-authors include Marjan De Mey, Wim Soetaert, Joeri Beauprez, Brecht De Paepe, Pieter Coussement, Erick Vandamme, Peter A. Vanrolleghem, Hendrik Waegeman, Daniël Charlier and Joseph J. Heijnen and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Jo Maertens

48 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jo Maertens Belgium 25 1.3k 276 217 155 137 48 1.6k
Solvej Siedler Denmark 16 1.1k 0.9× 160 0.6× 225 1.0× 160 1.0× 267 1.9× 18 1.4k
Byung‐Gee Kim South Korea 21 688 0.5× 80 0.3× 186 0.9× 119 0.8× 111 0.8× 40 1.1k
Xinxiao Sun China 24 1.4k 1.1× 97 0.4× 596 2.7× 272 1.8× 108 0.8× 77 1.8k
Zhang Wei-guo China 23 931 0.7× 92 0.3× 441 2.0× 170 1.1× 147 1.1× 66 1.3k
Yeong-Su Kim South Korea 22 925 0.7× 108 0.4× 338 1.6× 334 2.2× 78 0.6× 99 1.7k
Jinlei Tang China 16 856 0.7× 115 0.4× 191 0.9× 80 0.5× 49 0.4× 26 1.1k
Guang-Rong Zhao China 24 1.2k 0.9× 64 0.2× 155 0.7× 203 1.3× 192 1.4× 71 1.7k
Leonardo Rios‐Solis United Kingdom 22 937 0.7× 57 0.2× 301 1.4× 126 0.8× 299 2.2× 59 1.4k
Weizhu Zeng China 25 1.4k 1.1× 48 0.2× 394 1.8× 245 1.6× 207 1.5× 98 1.7k
Xiao‐Wei Yu China 27 1.3k 1.0× 55 0.2× 402 1.9× 394 2.5× 366 2.7× 97 1.9k

Countries citing papers authored by Jo Maertens

Since Specialization
Citations

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

Fields of papers citing papers by Jo Maertens

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jo Maertens

This figure shows the co-authorship network connecting the top 25 collaborators of Jo Maertens. A scholar is included among the top collaborators of Jo Maertens 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 Jo Maertens. Jo Maertens 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.
Guidi, Chiara, et al.. (2022). Dynamic feedback regulation for efficient membrane protein production using a small RNA-based genetic circuit in Escherichia coli. Microbial Cell Factories. 21(1). 260–260. 5 indexed citations
2.
Coussement, Pieter, et al.. (2020). Mapping and refactoring pathway control through metabolic and protein engineering: The hexosamine biosynthesis pathway. Biotechnology Advances. 40. 107512–107512. 20 indexed citations
3.
Coussement, Pieter, et al.. (2019). Combinatorial Assembly of Multigene Pathways by Combining Single-Strand Assembly with Golden Gate Assembly. Methods in molecular biology. 1927. 111–123. 2 indexed citations
4.
Maertens, Jo, et al.. (2018). Exploring of the feature space of de novo developed post-transcriptional riboregulators. PLoS Computational Biology. 14(8). e1006170–e1006170. 4 indexed citations
5.
Maertens, Jo, et al.. (2015). Metabolic engineering of Escherichia coli into a versatile glycosylation platform: production of bio-active quercetin glycosides. Microbial Cell Factories. 14(1). 138–138. 64 indexed citations
6.
Taymaz‐Nikerel, Hilal, Marjan De Mey, Gino Baart, et al.. (2015). Comparative fluxome and metabolome analysis for overproduction of succinate in Escherichia coli. Biotechnology and Bioengineering. 113(4). 817–829. 11 indexed citations
7.
Coussement, Pieter, et al.. (2015). Putting RNA to work: Translating RNA fundamentals into biotechnological engineering practice. Biotechnology Advances. 33(8). 1829–1844. 15 indexed citations
8.
Coussement, Pieter, et al.. (2014). One step DNA assembly for combinatorial metabolic engineering. Metabolic Engineering. 23. 70–77. 46 indexed citations
9.
10.
Waegeman, Hendrik, et al.. (2011). Increasing recombinant protein production in Escherichia coli K12 through metabolic engineering. New Biotechnology. 30(2). 255–261. 27 indexed citations
11.
Waegeman, Hendrik, Jo Maertens, Joeri Beauprez, Marjan De Mey, & Wim Soetaert. (2011). Effect of iclR and arcA deletions on physiology and metabolic fluxes in Escherichia coli BL21 (DE3). Biotechnology Letters. 34(2). 329–337. 24 indexed citations
12.
Mey, Marjan De, Jo Maertens, Wim Soetaert, et al.. (2010). Promoter knock-in: a novel rational method for the fine tuning of genes. BMC Biotechnology. 10(1). 26–26. 38 indexed citations
13.
Cerdobbel, An, Tom Desmet, Karel De Winter, Jo Maertens, & Wim Soetaert. (2010). Increasing the thermostability of sucrose phosphorylase by multipoint covalent immobilization. Journal of Biotechnology. 150(1). 125–130. 41 indexed citations
14.
Mey, Marjan De, Hilal Taymaz‐Nikerel, Gino Baart, et al.. (2010). Catching prompt metabolite dynamics in Escherichia coli with the BioScope at oxygen rich conditions. Metabolic Engineering. 12(5). 477–487. 30 indexed citations
15.
Maertens, Jo & Peter A. Vanrolleghem. (2010). Modeling with a view to target identification in metabolic engineering: A critical evaluation of the available tools. Biotechnology Progress. 26(2). 313–331. 10 indexed citations
16.
Donckels, Brecht, Dirk J.W. De Pauw, Bernard De Baets, Jo Maertens, & Peter A. Vanrolleghem. (2008). An anticipatory approach to optimal experimental design for model discrimination. Chemometrics and Intelligent Laboratory Systems. 95(1). 53–63. 27 indexed citations
17.
Mey, Marjan De, Joeri Beauprez, Jo Maertens, et al.. (2007). Comparison of Different Strategies to Reduce Acetate Formation in Escherichia coli. Biotechnology Progress. 0(0). 0–0. 43 indexed citations
18.
Mey, Marjan De, et al.. (2007). Comparison of protein quantification and extraction methods suitable for E. coli cultures. Biologicals. 36(3). 198–202. 21 indexed citations
19.
Mey, Marjan De, et al.. (2006). Comparison of DNA and RNA quantification methods suitable for parameter estimation in metabolic modeling of microorganisms. Analytical Biochemistry. 353(2). 198–203. 32 indexed citations
20.
Hulle, Stijn Van, et al.. (2004). Using parameter sensitivity analysis of the CANON biofilm process: What to measure, where to measure and under which conditions?. Ghent University Academic Bibliography (Ghent University). 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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