David Gacquer

1.2k total citations
18 papers, 766 citations indexed

About

David Gacquer is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, David Gacquer has authored 18 papers receiving a total of 766 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 4 papers in Endocrinology, Diabetes and Metabolism and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in David Gacquer's work include Thyroid Cancer Diagnosis and Treatment (4 papers), RNA Research and Splicing (3 papers) and Gene expression and cancer classification (3 papers). David Gacquer is often cited by papers focused on Thyroid Cancer Diagnosis and Treatment (4 papers), RNA Research and Splicing (3 papers) and Gene expression and cancer classification (3 papers). David Gacquer collaborates with scholars based in Belgium, France and United States. David Gacquer's co-authors include Vincent Detours, Angéline Bilheu, Julian Chéron, Devesh Kumar, Ikuo Suzuki, Marta Wojno, Pierre Vanderhaeghen, Carine Maenhaut, Roxane Van Heurck and Adèle Herpoel and has published in prestigious journals such as Cell, The Journal of Clinical Endocrinology & Metabolism and Cancer Research.

In The Last Decade

David Gacquer

16 papers receiving 755 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Gacquer Belgium 11 582 166 103 101 75 18 766
Stefanie Engler Switzerland 10 560 1.0× 122 0.7× 127 1.2× 68 0.7× 69 0.9× 15 973
Daniel G. Pankratz United States 11 559 1.0× 108 0.7× 211 2.0× 247 2.4× 23 0.3× 17 1.0k
Philip Brennecke Germany 5 940 1.6× 204 1.2× 89 0.9× 39 0.4× 33 0.4× 5 1.1k
Vida Senkus Melvin United States 9 470 0.8× 86 0.5× 323 3.1× 38 0.4× 52 0.7× 11 815
Santosh K. Mishra United States 12 420 0.7× 178 1.1× 137 1.3× 26 0.3× 42 0.6× 17 710
Holger Hiemisch Germany 11 575 1.0× 64 0.4× 193 1.9× 35 0.3× 26 0.3× 14 809
Pratheesh Sathyan United States 12 623 1.1× 514 3.1× 75 0.7× 16 0.2× 52 0.7× 26 1.1k
Benjamin Turgeon Canada 12 582 1.0× 62 0.4× 90 0.9× 89 0.9× 12 0.2× 14 781
Rongxin Fang United States 12 1.3k 2.2× 356 2.1× 219 2.1× 32 0.3× 23 0.3× 17 1.5k
Karen Pedersen Denmark 12 329 0.6× 53 0.3× 108 1.0× 22 0.2× 44 0.6× 20 641

Countries citing papers authored by David Gacquer

Since Specialization
Citations

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

Fields of papers citing papers by David Gacquer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Gacquer

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

All Works

18 of 18 papers shown
1.
Wang, Xiaoxiao, David Venet, Denis Larsimont, et al.. (2022). 1711P Spatial transcriptomics reveals substantial heterogeneity in TNBC tumor and stroma compartments with potential clinical implications. Annals of Oncology. 33. S1321–S1322. 1 indexed citations
2.
Collet, L., Mattia Rediti, David Venet, et al.. (2022). Abstract PD14-02: Unravelling spatial tumor organization and heterogeneity in lobular breast cancer using spatial transcriptomics. Cancer Research. 82(4_Supplement). PD14–2.
3.
Kyrilli, Aglaia, David Gacquer, Vincent Detours, et al.. (2019). Dissecting the Role of Thyrotropin in the DNA Damage Response in Human Thyrocytes after 131I, γ Radiation and H2O2. The Journal of Clinical Endocrinology & Metabolism. 105(3). 839–853. 6 indexed citations
4.
Suzuki, Ikuo, David Gacquer, Roxane Van Heurck, et al.. (2018). Human-Specific NOTCH2NL Genes Expand Cortical Neurogenesis through Delta/Notch Regulation. Cell. 173(6). 1370–1384.e16. 255 indexed citations
5.
Trubiroha, Achim, David Gacquer, Frédérick Libert, et al.. (2018). A Rapid CRISPR/Cas-based Mutagenesis Assay in Zebrafish for Identification of Genes Involved in Thyroid Morphogenesis and Function. Scientific Reports. 8(1). 5647–5647. 37 indexed citations
6.
Tarabichi, Maxime, Aline Antoniou, David Gacquer, et al.. (2018). Distinctive Desmoplastic 3D Morphology Associated With BRAFV600E in Papillary Thyroid Cancers. The Journal of Clinical Endocrinology & Metabolism. 103(3). 1102–1111. 10 indexed citations
7.
Fimereli, Danai, Debora Fumagalli, David N. Brown, et al.. (2018). Genomic hotspots but few recurrent fusion genes in breast cancer. Genes Chromosomes and Cancer. 57(7). 331–338. 15 indexed citations
8.
Saiselet, Manuel, David Gacquer, Ligia Craciun, et al.. (2015). New global analysis of the microRNA transcriptome of primary tumors and lymph node metastases of papillary thyroid cancer. BMC Genomics. 16(1). 828–828. 57 indexed citations
9.
Fimereli, Danai, David Gacquer, Debora Fumagalli, et al.. (2015). No significant viral transcription detected in whole breast cancer transcriptomes. BMC Cancer. 15(1). 147–147. 17 indexed citations
10.
Konopka, Tomasz, David Gacquer, Danai Fimereli, et al.. (2015). Intratumor heterogeneity and clonal evolution in an aggressive papillary thyroid cancer and matched metastases. Endocrine Related Cancer. 22(2). 205–216. 23 indexed citations
11.
Fumagalli, Debora, David Gacquer, Françoise Rothé, et al.. (2015). Principles Governing A-to-I RNA Editing in the Breast Cancer Transcriptome. Cell Reports. 13(2). 277–289. 171 indexed citations
12.
Fumagalli, Debora, Alexis Blanchet-Cohen, David N. Brown, et al.. (2014). Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology. BMC Genomics. 15(1). 1008–1008. 42 indexed citations
13.
Tarabichi, Maxime, David Gacquer, Aline Hébrant, et al.. (2012). A general method to derive robust organ-specific gene expression-based differentiation indices: application to thyroid cancer diagnostic. Oncogene. 31(41). 4490–4498. 106 indexed citations
14.
Fumagalli, Debora, Benjamin Haibe‐Kains, Stefan Michiels, et al.. (2012). Abstract P3-04-10: Comparison between RNA-Seq and Affymetrix gene expression data. Cancer Research. 72(24_Supplement). P3–4. 1 indexed citations
15.
Gacquer, David, et al.. (2011). Comparative study of supervised classification algorithms for the detection of atmospheric pollution. Engineering Applications of Artificial Intelligence. 24(6). 1070–1083. 14 indexed citations
16.
Gacquer, David, et al.. (2008). A genetic approach for training diverse classifier ensembles. 1 indexed citations
17.
Delmotte, François & David Gacquer. (2008). Detection of defective sources with belief function s. 9 indexed citations
18.
Gacquer, David, et al.. (2006). Comparison of Several Classifiers for the Detection of Polluting Smokes. 50. 146–146. 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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