Johann de Jong

1.4k total citations
23 papers, 865 citations indexed

About

Johann de Jong is a scholar working on Molecular Biology, Artificial Intelligence and Plant Science. According to data from OpenAlex, Johann de Jong has authored 23 papers receiving a total of 865 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 7 papers in Artificial Intelligence and 4 papers in Plant Science. Recurrent topics in Johann de Jong's work include Machine Learning in Healthcare (7 papers), CRISPR and Genetic Engineering (5 papers) and Genomics and Chromatin Dynamics (5 papers). Johann de Jong is often cited by papers focused on Machine Learning in Healthcare (7 papers), CRISPR and Genetic Engineering (5 papers) and Genomics and Chromatin Dynamics (5 papers). Johann de Jong collaborates with scholars based in Netherlands, United States and Germany. Johann de Jong's co-authors include Lodewyk F.A. Wessels, Waseem Akhtar, Maarten van Lohuizen, Jeroen de Ridder, Alexey V. Pindyurin, Anton Berns, Ludo Pagie, Bas van Steensel, Wouter Meuleman and Holger Fröhlich and has published in prestigious journals such as Cell, Nucleic Acids Research and Nature Communications.

In The Last Decade

Johann de Jong

22 papers receiving 852 citations

Peers

Johann de Jong

GENET

ONCOL

Vicky Yao United States

Vibhor Kumar Singapore

Noah Davidsohn United States

Thomas Westerling United States

Naoki Nariai Japan

Zhiyuan Sun China

Michael J. Lawson United States

Jean Monlong United States

Yvonne M. Bradford United States

Irene Papatheodorou United Kingdom

Vicky Yao United States

Johann de Jong

611 ×1.0

205 ×1.5

GENET

88 ×1.0

ONCOL

31 ×0.4

33 ×0.5

Citations per year, relative to Johann de Jong Johann de Jong (= 1×) peers Vicky Yao

Countries citing papers authored by Johann de Jong

Since Specialization

Citations

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

Fields of papers citing papers by Johann de Jong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Johann de Jong. 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 Johann de Jong. The network helps show where Johann de Jong may publish in the future.

Co-authorship network of co-authors of Johann de Jong

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

All Works

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20 of 20 papers shown

Qiu, Jiajun, Li Li, A. Mesut Erzurumluoglu, et al.. (2025). Deep representation learning for clustering longitudinal survival data from electronic health records. Nature Communications. 16(1). 2534–2534. 2 indexed citations

Arora, Jatin, A. Mesut Erzurumluoglu, Stephen A. Stanhope, et al.. (2024). Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction. Journal of the American Medical Informatics Association. 32(3). 435–446.

Beaulieu‐Jones, Brett K., Mauricio F. Villamar, Phil Scordis, et al.. (2023). Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study. The Lancet Digital Health. 5(12). e882–e894. 16 indexed citations

Jong, Johann de, et al.. (2021). An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data. Frontiers in Artificial Intelligence. 4. 610197–610197. 12 indexed citations

Gonçalves, Joana P., Waseem Akhtar, Johann de Jong, et al.. (2019). Multiplexed Cas9 targeting reveals genomic location effects and gRNA-based staggered breaks influencing mutation efficiency. Nature Communications. 10(1). 1598–1598. 40 indexed citations

Jong, Johann de, Ping Wu, Reagon Karki, et al.. (2019). Deep learning for clustering of multivariate clinical patient trajectories with missing values. GigaScience. 8(11). 48 indexed citations

Schilder-Tol, Esther J.M., Monique E.C.M. Oud, Esther A. Beuling, et al.. (2016). The small FOXP1 isoform predominantly expressed in activated B cell-like diffuse large B-cell lymphoma and full-length FOXP1 exert similar oncogenic and transcriptional activity in human B cells. Haematologica. 102(3). 573–583. 14 indexed citations

Babaei, Sepideh, Waseem Akhtar, Johann de Jong, Marcel Reinders, & Jeroen de Ridder. (2015). 3D hotspots of recurrent retroviral insertions reveal long-range interactions with cancer genes. Nature Communications. 6(1). 6381–6381. 25 indexed citations

Pang, Baoxu, Johann de Jong, Xiaohang Qiao, Lodewyk F.A. Wessels, & Jacques Neefjes. (2015). Chemical profiling of the genome with anti-cancer drugs defines target specificities. Nature Chemical Biology. 11(7). 472–480. 59 indexed citations

10.

Jansson, Fredrik, et al.. (2015). Analysis of a spatial gene expression database for sea anemone Nematostella vectensis during early development. BMC Systems Biology. 9(1). 63–63. 4 indexed citations

11.

Pindyurin, Alexey V., Johann de Jong, & Waseem Akhtar. (2015). TRIP through the chromatin: A high throughput exploration of enhancer regulatory landscapes. Genomics. 106(3). 171–177. 2 indexed citations

12.

Vries, Nienke A. de, Danielle Hulsman, Waseem Akhtar, et al.. (2015). Prolonged Ezh2 Depletion in Glioblastoma Causes a Robust Switch in Cell Fate Resulting in Tumor Progression. Cell Reports. 10(3). 383–397. 71 indexed citations

13.

Akhtar, Waseem, Alexey V. Pindyurin, Johann de Jong, et al.. (2014). Using TRIP for genome-wide position effect analysis in cultured cells. Nature Protocols. 9(6). 1255–1281. 31 indexed citations

14.

Röttinger, Éric, et al.. (2014). A Computational Approach towards a Gene Regulatory Network for the Developing Nematostella vectensis Gut. PLoS ONE. 9(7). e103341–e103341. 11 indexed citations

15.

Jong, Johann de, Lodewyk F.A. Wessels, Maarten van Lohuizen, Jeroen de Ridder, & Waseem Akhtar. (2014). Applications of DNA integrating elements: Facing the bias bully. Mobile Genetic Elements. 4(6). 1–6. 6 indexed citations

16.

Jong, Johann de, Waseem Akhtar, Jitendra Badhai, et al.. (2014). Chromatin Landscapes of Retroviral and Transposon Integration Profiles. PLoS Genetics. 10(4). e1004250–e1004250. 71 indexed citations

17.

Akhtar, Waseem, Johann de Jong, Alexey V. Pindyurin, et al.. (2013). Chromatin Position Effects Assayed by Thousands of Reporters Integrated in Parallel. Cell. 154(4). 914–927. 235 indexed citations

18.

Heideman, Marinus R., Roel H. Wilting, Eva Yanover, et al.. (2013). Dosage-dependent tumor suppression by histone deacetylases 1 and 2 through regulation of c-Myc collaborating genes and p53 function. Blood. 121(11). 2038–2050. 79 indexed citations

19.

Jong, Johann de, Jeroen de Ridder, Louise van der Weyden, et al.. (2011). Computational identification of insertional mutagenesis targets for cancer gene discovery. Nucleic Acids Research. 39(15). e105–e105. 23 indexed citations

20.

Postma, Marten, et al.. (2010). A cell-based model of Nematostella vectensis gastrulation including bottle cell formation, invagination and zippering. Developmental Biology. 351(1). 217–228. 46 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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