Veerle W. Daniëls

1.5k total citations · 1 hit paper
8 papers, 906 citations indexed

About

Veerle W. Daniëls is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Veerle W. Daniëls has authored 8 papers receiving a total of 906 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Cancer Research and 3 papers in Oncology. Recurrent topics in Veerle W. Daniëls's work include Cancer, Hypoxia, and Metabolism (4 papers), Epigenetics and DNA Methylation (2 papers) and Cancer, Lipids, and Metabolism (2 papers). Veerle W. Daniëls is often cited by papers focused on Cancer, Hypoxia, and Metabolism (4 papers), Epigenetics and DNA Methylation (2 papers) and Cancer, Lipids, and Metabolism (2 papers). Veerle W. Daniëls collaborates with scholars based in United States, Belgium and France. Veerle W. Daniëls's co-authors include Johannes V. Swinnen, Karine Smans, Guido Verhoeven, Sebastian Munck, Frank Vanderhoydonc, Etienne Waelkens, Paul P. Van Veldhoven, David Waltregny, Evelien Rysman and Jelle Machiels and has published in prestigious journals such as Nature Medicine, PLoS ONE and Cancer Research.

In The Last Decade

Veerle W. Daniëls

8 papers receiving 899 citations

Hit Papers

De novo Lipogenesis Protects Cancer Cells from Free Radic... 2010 2026 2015 2020 2010 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Veerle W. Daniëls United States 7 591 553 112 110 101 8 906
Sminu Bose United States 6 849 1.4× 748 1.4× 179 1.6× 103 0.9× 88 0.9× 14 1.2k
Ibtissam Marchiq France 12 939 1.6× 756 1.4× 198 1.8× 108 1.0× 89 0.9× 16 1.3k
Ylenia Perone Italy 8 397 0.7× 351 0.6× 120 1.1× 91 0.8× 45 0.4× 11 819
Yanling Jing China 10 669 1.1× 468 0.8× 140 1.3× 67 0.6× 43 0.4× 17 929
Gina N. Alesi United States 7 612 1.0× 474 0.9× 148 1.3× 124 1.1× 53 0.5× 9 898
Sushama Kamarajugadda United States 9 635 1.1× 528 1.0× 185 1.7× 47 0.4× 48 0.5× 10 930
Megan D. Hoeksema United States 13 794 1.3× 552 1.0× 210 1.9× 278 2.5× 215 2.1× 13 1.2k
Yakir Guri Switzerland 7 567 1.0× 251 0.5× 113 1.0× 49 0.4× 59 0.6× 8 778
Bradford K. Harris United States 9 628 1.1× 492 0.9× 175 1.6× 230 2.1× 210 2.1× 10 958
Lou Baudrier United States 7 642 1.1× 503 0.9× 111 1.0× 172 1.6× 81 0.8× 10 890

Countries citing papers authored by Veerle W. Daniëls

Since Specialization
Citations

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

Fields of papers citing papers by Veerle W. Daniëls

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Veerle W. Daniëls. 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 Veerle W. Daniëls. The network helps show where Veerle W. Daniëls may publish in the future.

Co-authorship network of co-authors of Veerle W. Daniëls

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

All Works

8 of 8 papers shown
1.
Jochems, Fleur, J. Macdonald, Veerle W. Daniëls, et al.. (2024). Senolysis by ABT-263 is associated with inherent apoptotic dependence of cancer cells derived from the non-senescent state. Cell Death and Differentiation. 32(5). 855–865. 5 indexed citations
2.
Daniëls, Veerle W., Jason J. Zoeller, Nick van Gastel, et al.. (2021). Metabolic perturbations sensitize triple-negative breast cancers to apoptosis induced by BH3 mimetics. Science Signaling. 14(686). 22 indexed citations
3.
Zoeller, Jason J., Veerle W. Daniëls, Krishan Taneja, et al.. (2020). Navitoclax enhances the effectiveness of EGFR-targeted antibody-drug conjugates in PDX models of EGFR-expressing triple-negative breast cancer. Breast Cancer Research. 22(1). 132–132. 26 indexed citations
4.
Wilson, X., Veerle W. Daniëls, Lisa Ta, et al.. (2017). Cytoplasmic p53 couples oncogene-driven glucose metabolism to apoptosis and is a therapeutic target in glioblastoma. Nature Medicine. 23(11). 1342–1351. 84 indexed citations
5.
Khan, Niamat, Ali Talebi, Arnaud Marchand, et al.. (2016). Identification of drugs that restore primary cilium expression in cancer cells. Oncotarget. 7(9). 9975–9992. 61 indexed citations
6.
Daniëls, Veerle W., Karine Smans, Inès Royaux, et al.. (2014). Cancer Cells Differentially Activate and Thrive on De Novo Lipid Synthesis Pathways in a Low-Lipid Environment. PLoS ONE. 9(9). e106913–e106913. 89 indexed citations
7.
Gendt, Karel De, Evi Denolet, Ariane Willems, et al.. (2011). Expression of Tubb3, a Beta-Tubulin Isotype, Is Regulated by Androgens in Mouse and Rat Sertoli Cells1. Biology of Reproduction. 85(5). 934–945. 47 indexed citations
8.
Rysman, Evelien, Koen Brusselmans, Katryn Scheys, et al.. (2010). De novo Lipogenesis Protects Cancer Cells from Free Radicals and Chemotherapeutics by Promoting Membrane Lipid Saturation. Cancer Research. 70(20). 8117–8126. 572 indexed citations breakdown →

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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