Gert Thijs

3.2k total citations
21 papers, 1.3k citations indexed

About

Gert Thijs is a scholar working on Molecular Biology, Genetics and Computational Theory and Mathematics. According to data from OpenAlex, Gert Thijs has authored 21 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 5 papers in Genetics and 2 papers in Computational Theory and Mathematics. Recurrent topics in Gert Thijs's work include Genomics and Chromatin Dynamics (11 papers), Gene expression and cancer classification (9 papers) and Genomics and Phylogenetic Studies (7 papers). Gert Thijs is often cited by papers focused on Genomics and Chromatin Dynamics (11 papers), Gene expression and cancer classification (9 papers) and Genomics and Phylogenetic Studies (7 papers). Gert Thijs collaborates with scholars based in Belgium, United States and Denmark. Gert Thijs's co-authors include Yves Moreau, Kathleen Marchal, Bart De Moor, Stéphane Rombauts, Magali Lescot, Pierre Rouzé, Bart De Moor, Stein Aerts, Peter Van Loo and Hans De Winter and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Proceedings of the IEEE.

In The Last Decade

Gert Thijs

20 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gert Thijs Belgium 14 1.1k 229 171 107 106 21 1.3k
Gabriella Rustici United Kingdom 16 1.5k 1.4× 204 0.9× 143 0.8× 102 1.0× 64 0.6× 26 1.9k
Nicole Redaschi Switzerland 14 1.0k 0.9× 89 0.4× 126 0.7× 102 1.0× 148 1.4× 29 1.3k
Michele Magrane United Kingdom 14 1.7k 1.5× 192 0.8× 211 1.2× 116 1.1× 163 1.5× 21 2.2k
Daniel H. Lackner United Kingdom 16 1.7k 1.6× 129 0.6× 143 0.8× 51 0.5× 91 0.9× 20 1.9k
Rama Balakrishnan United States 14 1.4k 1.3× 158 0.7× 139 0.8× 91 0.9× 68 0.6× 24 1.6k
Guojun Li China 19 849 0.8× 140 0.6× 152 0.9× 75 0.7× 212 2.0× 93 1.4k
Michael Yu United States 15 1.0k 0.9× 79 0.3× 172 1.0× 90 0.8× 121 1.1× 23 1.4k
Vasilis J. Promponas Cyprus 18 994 0.9× 183 0.8× 121 0.7× 58 0.5× 36 0.3× 60 1.5k
Preston W. Estep United States 10 2.2k 2.0× 251 1.1× 362 2.1× 138 1.3× 40 0.4× 16 2.5k
Castrense Savojardo Italy 19 1.2k 1.1× 215 0.9× 205 1.2× 32 0.3× 106 1.0× 65 1.6k

Countries citing papers authored by Gert Thijs

Since Specialization
Citations

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

Fields of papers citing papers by Gert Thijs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gert Thijs

This figure shows the co-authorship network connecting the top 25 collaborators of Gert Thijs. A scholar is included among the top collaborators of Gert Thijs 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 Gert Thijs. Gert Thijs 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.
2.
Sukhai, Mahadeo A., Maksym Misyura, Mariam Thomas, et al.. (2018). Somatic Tumor Variant Filtration Strategies to Optimize Tumor-Only Molecular Profiling Using Targeted Next-Generation Sequencing Panels. Journal of Molecular Diagnostics. 21(2). 261–273. 29 indexed citations
3.
Taminau, Jonatan, Gert Thijs, & Hans De Winter. (2008). Pharao: Pharmacophore alignment and optimization. Journal of Molecular Graphics and Modelling. 27(2). 161–169. 96 indexed citations
4.
Thijs, Gert, et al.. (2006). Applications of Gibbs sampling in bioinformatics. Optimization methods & software. 1 indexed citations
5.
Monsieurs, Pieter, Gert Thijs, Abeer Fadda, et al.. (2006). More robust detection of motifs in coexpressed genes by using phylogenetic information. BMC Bioinformatics. 7(1). 160–160. 13 indexed citations
6.
Monsieurs, Pieter, et al.. (2005). A novel approach to identifying regulatory motifs in distantly related genomes.. Genome Biology. 6(13). R113–R113. 10 indexed citations
7.
Aerts, Stein, Peter Van Loo, Gert Thijs, et al.. (2005). TOUCAN 2: the all-inclusive open source workbench for regulatory sequence analysis. Nucleic Acids Research. 33(Web Server). W393–W396. 118 indexed citations
8.
Marchal, Kathleen, Sigrid C. J. De Keersmaecker, Pieter Monsieurs, et al.. (2004). In silico identification and experimental validation of PmrAB targets in Salmonella typhimuriumby regulatory motif detection. Genome biology. 5(2). R9–R9. 49 indexed citations
9.
Aerts, Stein, Gert Thijs, Michał Dąbrowski, Yves Moreau, & Bart De Moor. (2004). Comprehensive analysis of the base composition around the transcription start site in Metazoa. BMC Genomics. 5(1). 34–34. 58 indexed citations
10.
Marchal, Kathleen, Gert Thijs, Sigrid C. J. De Keersmaecker, et al.. (2003). Genome-specific higher-order background models to improve motif detection. Trends in Microbiology. 11(2). 61–66. 23 indexed citations
11.
Aerts, Stein, Peter Van Loo, Gert Thijs, Yves Moreau, & Bart De Moor. (2003). Computational detection of cis -regulatory modules. Bioinformatics. 19(suppl_2). ii5–ii14. 101 indexed citations
12.
Thijs, Gert, Yves Moreau, Frank De Smet, et al.. (2002). INCLUSive: INtegrated Clustering, Upstream sequence retrieval and motif Sampling. Bioinformatics. 18(2). 331–332. 64 indexed citations
13.
Smet, Frank De, Janick Mathys, Kathleen Marchal, et al.. (2002). Adaptive quality-based clustering of gene expression profiles. Bioinformatics. 18(5). 735–746. 128 indexed citations
14.
Thijs, Gert, Kathleen Marchal, Magali Lescot, et al.. (2002). A Gibbs Sampling Method to Detect Overrepresented Motifs in the Upstream Regions of Coexpressed Genes. Journal of Computational Biology. 9(2). 447–464. 267 indexed citations
15.
Moreau, Yves, Frank De Smet, Gert Thijs, Kathleen Marchal, & Bart De Moor. (2002). Functional bioinformatics of microarray data: from expression to regulation. Proceedings of the IEEE. 90(11). 1722–1743. 30 indexed citations
16.
Moreau, Yves, Gert Thijs, Kathleen Marchal, et al.. (2002). Integrating quality-based clustering of microarray data with Gibbs sampling for the discovery of regulatory motifs. Ghent University Academic Bibliography (Ghent University). 75–79. 1 indexed citations
17.
Thijs, Gert, Kathleen Marchal, Magali Lescot, et al.. (2001). A Gibbs sampling method to detect over-represented motifs in the upstream regions of co-expressed genes. 305–312. 46 indexed citations
18.
Thijs, Gert, Magali Lescot, Kathleen Marchal, et al.. (2001). A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics. 17(12). 1113–1122. 287 indexed citations
19.
Thijs, Gert, Stéphane Rombauts, Magali Lescot, et al.. (2000). Detection of cis-acting regulatory elements in plants : a GIBBS sampling approach. Ghent University Academic Bibliography (Ghent University). 4 indexed citations
20.
Thijs, Gert. (1999). Recognition of gene regulatory sequences by bagging of neural networks. 1999. 988–993. 1 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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