Sander Canisius

4.1k total citations
36 papers, 875 citations indexed

About

Sander Canisius is a scholar working on Artificial Intelligence, Molecular Biology and Genetics. According to data from OpenAlex, Sander Canisius has authored 36 papers receiving a total of 875 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 13 papers in Molecular Biology and 9 papers in Genetics. Recurrent topics in Sander Canisius's work include Natural Language Processing Techniques (14 papers), Topic Modeling (12 papers) and Estrogen and related hormone effects (6 papers). Sander Canisius is often cited by papers focused on Natural Language Processing Techniques (14 papers), Topic Modeling (12 papers) and Estrogen and related hormone effects (6 papers). Sander Canisius collaborates with scholars based in Netherlands, United Kingdom and United States. Sander Canisius's co-authors include Lodewyk F.A. Wessels, Antal van den Bosch, Wilbert Zwart, Walter Daelemans, John W.M. Martens, Jason S. Carroll, Sabine C. Linn, Vassiliki Theodorou, Marleen Kok and Linda Henneman and has published in prestigious journals such as Cell, The EMBO Journal and Cancer Research.

In The Last Decade

Sander Canisius

31 papers receiving 832 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sander Canisius Netherlands 14 404 190 168 156 143 36 875
Ming Shao China 19 602 1.5× 114 0.6× 143 0.9× 83 0.5× 100 0.7× 67 1.2k
Gene Cutler United States 13 598 1.5× 44 0.2× 86 0.5× 167 1.1× 129 0.9× 18 1.1k
Ricardo Villamarín-Salomón United States 5 1.1k 2.8× 151 0.8× 289 1.7× 138 0.9× 780 5.5× 8 1.9k
Jianpeng Chen China 13 292 0.7× 59 0.3× 145 0.9× 114 0.7× 13 0.1× 42 641
Pouya Khankhanian United States 16 666 1.6× 107 0.6× 65 0.4× 83 0.5× 292 2.0× 40 1.3k
Shiro Kadowaki Japan 4 638 1.6× 90 0.5× 25 0.1× 46 0.3× 33 0.2× 4 946
Catherine Leroy France 18 694 1.7× 19 0.1× 86 0.5× 174 1.1× 56 0.4× 33 1.0k
Terrence F. Meehan United States 14 572 1.4× 67 0.4× 58 0.3× 66 0.4× 122 0.9× 20 904
Dirk Fey Ireland 17 791 2.0× 29 0.2× 129 0.8× 190 1.2× 49 0.3× 44 1.4k
Andra Waagmeester Netherlands 13 862 2.1× 127 0.7× 140 0.8× 80 0.5× 109 0.8× 30 1.2k

Countries citing papers authored by Sander Canisius

Since Specialization
Citations

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

Fields of papers citing papers by Sander Canisius

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sander Canisius

This figure shows the co-authorship network connecting the top 25 collaborators of Sander Canisius. A scholar is included among the top collaborators of Sander Canisius 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 Sander Canisius. Sander Canisius 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.
Burgess, Stephen, et al.. (2025). Causal Effects of Breast Cancer Risk Factors across Hormone Receptor Breast Cancer Subtypes: A Two-Sample Mendelian Randomization Study. Cancer Epidemiology Biomarkers & Prevention. 34(6). 933–943.
2.
Schmidt, Marjanka K., et al.. (2024). Assessing the validity of driver gene identification tools for targeted genome sequencing data. Bioinformatics Advances. 4(1). vbae073–vbae073. 1 indexed citations
3.
4.
Morra, Anna, Sander Canisius, Jenny Chang‐Claude, et al.. (2020). Breast cancer risk factors and their effects on survival: a Mendelian randomisation study. BMC Medicine. 18(1). 327–327. 57 indexed citations
5.
Bismeijer, Tycho, Bas H. M. van der Velden, Sander Canisius, et al.. (2020). Radiogenomic Analysis of Breast Cancer by Linking MRI Phenotypes with Tumor Gene Expression. Radiology. 296(2). 277–287. 39 indexed citations
6.
Canisius, Sander, Lodewyk F.A. Wessels, Marcel Smid, et al.. (2020). Integrative analysis of genomic amplification-dependent expression and loss-of-function screen identifies ASAP1 as a driver gene in triple-negative breast cancer progression. Oncogene. 39(20). 4118–4131. 19 indexed citations
7.
Velden, Bas H. M. van der, Tycho Bismeijer, Sander Canisius, et al.. (2019). Are contralateral parenchymal enhancement on dynamic contrast-enhanced MRI and genomic ER-pathway activity in ER-positive/HER2-negative breast cancer related?. European Journal of Radiology. 121. 108705–108705. 8 indexed citations
8.
Bismeijer, Tycho, Sander Canisius, & Lodewyk F.A. Wessels. (2018). Molecular characterization of breast and lung tumors by integration of multiple data types with functional sparse-factor analysis. PLoS Computational Biology. 14(10). e1006520–e1006520. 11 indexed citations
9.
Canisius, Sander, John W.M. Martens, & Lodewyk F.A. Wessels. (2016). A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome biology. 17(1). 261–261. 74 indexed citations
10.
Zwart, Wilbert, Koen D. Flach, Tarek M.A. Abdel-Fatah, et al.. (2015). SRC3 Phosphorylation at Serine 543 Is a Positive Independent Prognostic Factor in ER-Positive Breast Cancer. Clinical Cancer Research. 22(2). 479–491. 14 indexed citations
11.
Oosterkamp, Hendrika M., E. Marielle Hijmans, Thijn R. Brummelkamp, et al.. (2014). USP9X Downregulation Renders Breast Cancer Cells Resistant to Tamoxifen. Cancer Research. 74(14). 3810–3820. 37 indexed citations
12.
Mittempergher, Lorenza, Mahasti Saghatchian, Denise M. Wolf, et al.. (2013). A gene signature for late distant metastasis in breast cancer identifies a potential mechanism of late recurrences. Molecular Oncology. 7(5). 987–999. 38 indexed citations
13.
Middelbeek, Jeroen, Arthur J. Kuipers, Linda Henneman, et al.. (2012). TRPM7 Is Required for Breast Tumor Cell Metastasis. Cancer Research. 72(16). 4250–4261. 176 indexed citations
14.
Leeuw, Renée de, Koen D. Flach, Xanthippi Alexi, et al.. (2012). PKA phosphorylation redirects ERα to promoters of a unique gene set to induce tamoxifen resistance. Oncogene. 32(30). 3543–3551. 33 indexed citations
15.
Broek, Alexandra J. van den, Annegien Broeks, Hugo M. Horlings, et al.. (2011). Association of the germline TP53 R72P and MDM2 SNP309 variants with breast cancer survival in specific breast tumor subgroups. Breast Cancer Research and Treatment. 130(2). 599–608. 7 indexed citations
16.
Bosch, Antal van den, et al.. (2007). An efficient memory-based morphosyntactic tagger and parser for Dutch. Research portal (Tilburg University). 7. 191–206. 115 indexed citations
17.
Canisius, Sander & Caroline Sporleder. (2007). Bootstrapping Information Extraction from Field Books. Empirical Methods in Natural Language Processing. 827–836. 12 indexed citations
18.
Canisius, Sander & Erik Tjong Kim Sang. (2007). A Constraint Satisfaction Approach to Dependency Parsing. Research portal (Tilburg University). 1124–1128. 8 indexed citations
19.
Canisius, Sander & Antal van den Bosch. (2004). A memory-based shallow parser for spoken Dutch. Data Archiving and Networked Services (DANS). 31–45. 4 indexed citations
20.
Bosch, Antal van den, Sander Canisius, Walter Daelemans, Iris Hendrickx, & Erik F. Tjong Kim Sang. (2004). Memory-based semantic role labeling: Optimizing features, algorithm, and output. Data Archiving and Networked Services (DANS). 102–105. 11 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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