Jeroen P. Roose

8.6k total citations · 3 hit papers
66 papers, 6.4k citations indexed

About

Jeroen P. Roose is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Jeroen P. Roose has authored 66 papers receiving a total of 6.4k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 22 papers in Immunology and 19 papers in Oncology. Recurrent topics in Jeroen P. Roose's work include T-cell and B-cell Immunology (16 papers), Immune Cell Function and Interaction (13 papers) and Protein Kinase Regulation and GTPase Signaling (9 papers). Jeroen P. Roose is often cited by papers focused on T-cell and B-cell Immunology (16 papers), Immune Cell Function and Interaction (13 papers) and Protein Kinase Regulation and GTPase Signaling (9 papers). Jeroen P. Roose collaborates with scholars based in United States, Netherlands and Germany. Jeroen P. Roose's co-authors include Hans Clevers, Miranda Molenaar, Olivier Destrée, Marc van de Wetering, Vladimír Kořínek, Arthur Weiss, Josi Peterson-Maduro, Mariëtte A. Oosterwegel, Susan F. Godsave and Petra Moerer and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Jeroen P. Roose

66 papers receiving 6.3k citations

Hit Papers

XTcf-3 Transcription Factor Mediates β-Catenin-Induced Ax... 1996 2026 2006 2016 1996 1998 1998 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jeroen P. Roose United States 34 5.1k 1.1k 932 588 576 66 6.4k
George Hausmann Switzerland 27 3.8k 0.8× 669 0.6× 1.3k 1.4× 638 1.1× 712 1.2× 37 5.1k
Rikiro Fukunaga Japan 32 3.3k 0.7× 1.7k 1.5× 1.3k 1.4× 468 0.8× 831 1.4× 71 5.7k
Yojiro Yamanaka Canada 36 5.9k 1.2× 1.1k 0.9× 1.6k 1.7× 1.0k 1.8× 879 1.5× 67 7.6k
Rafael Pulido Spain 38 3.6k 0.7× 1.4k 1.3× 843 0.9× 586 1.0× 234 0.4× 130 5.3k
Marian L. Waterman United States 44 5.5k 1.1× 789 0.7× 1.1k 1.1× 484 0.8× 1.0k 1.8× 75 7.2k
Alessandra Eva Italy 35 3.5k 0.7× 1.1k 1.0× 1.0k 1.1× 1.1k 1.9× 579 1.0× 111 5.5k
Yuji Yamanashi Japan 43 3.5k 0.7× 2.3k 2.0× 1.2k 1.3× 817 1.4× 575 1.0× 94 7.1k
Adam Hurlstone United Kingdom 33 3.7k 0.7× 692 0.6× 1.1k 1.2× 1.3k 2.1× 551 1.0× 56 5.1k
Elizabeth J. Taparowsky United States 39 3.3k 0.7× 1.7k 1.5× 1.4k 1.5× 444 0.8× 658 1.1× 74 5.6k
Shigeyuki Nada Japan 34 4.7k 0.9× 1.2k 1.1× 853 0.9× 1.5k 2.6× 461 0.8× 71 6.8k

Countries citing papers authored by Jeroen P. Roose

Since Specialization
Citations

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

Fields of papers citing papers by Jeroen P. Roose

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeroen P. Roose. 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 Jeroen P. Roose. The network helps show where Jeroen P. Roose may publish in the future.

Co-authorship network of co-authors of Jeroen P. Roose

This figure shows the co-authorship network connecting the top 25 collaborators of Jeroen P. Roose. A scholar is included among the top collaborators of Jeroen P. Roose 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 Jeroen P. Roose. Jeroen P. Roose 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.
Wen, Zhi, Xin Gao, Lin Li, et al.. (2022). Tcof1 haploinsufficiency promotes early T cell precursor-like leukemia in NrasQ61R/+ mice. Leukemia. 36(4). 1167–1170. 1 indexed citations
2.
3.
Kondo, Yasushi, Jana Ognjenović, Saikat Banerjee, et al.. (2019). Cryo-EM structure of a dimeric B-Raf:14-3-3 complex reveals asymmetry in the active sites of B-Raf kinases. Science. 366(6461). 109–115. 118 indexed citations
4.
Bonnans, Caroline, Delphine Grün, Chih‐Yang Wang, et al.. (2019). RasGRP1 is a potential biomarker for stratifying anti-EGFR therapy response in colorectal cancer. JCI Insight. 4(15). 18 indexed citations
5.
Myers, Darienne R., et al.. (2019). mTOR and other effector kinase signals that impact T cell function and activity. Immunological Reviews. 291(1). 134–153. 60 indexed citations
6.
Vercoulen, Yvonne, Yasushi Kondo, Jeffrey S. Iwig, et al.. (2017). A Histidine pH sensor regulates activation of the Ras-specific guanine nucleotide exchange factor RasGRP1. eLife. 6. 29 indexed citations
7.
Myers, Darienne R., Hyung W. Lim, Herbert G. Kasler, et al.. (2017). Tonic LAT-HDAC7 Signals Sustain Nur77 and Irf4 Expression to Tune Naive CD4 T Cells. Cell Reports. 19(8). 1558–1571. 34 indexed citations
8.
Myers, Darienne R., Julie Zikherman, & Jeroen P. Roose. (2017). Tonic Signals: Why Do Lymphocytes Bother?. Trends in Immunology. 38(11). 844–857. 86 indexed citations
9.
Depeille, Philippe, Linda M. Henricks, Robert A. H. van de Ven, et al.. (2015). RasGRP1 opposes proliferative EGFR–SOS1–Ras signals and restricts intestinal epithelial cell growth. Nature Cell Biology. 17(6). 804–815. 52 indexed citations
10.
Yang, Ming, Christopher C. Govern, Arup K. Chakraborty, et al.. (2012). Dysregulated RasGRP1 Responds to Cytokine Receptor Input in T Cell Leukemogenesis. DSpace@MIT (Massachusetts Institute of Technology). 39 indexed citations
11.
Chakraborty, Arup K., Jayajit Das, Julie Zikherman, et al.. (2009). Molecular Origin and Functional Consequences of Digital Signaling and Hysteresis During Ras Activation in Lymphocytes. PubMed Central. 8 indexed citations
12.
Das, Jayajit, Mary Ho, Julie Zikherman, et al.. (2009). Digital Signaling and Hysteresis Characterize Ras Activation in Lymphoid Cells. Cell. 136(2). 337–351. 313 indexed citations
13.
Nirula, Ajay, Mary Ho, Hyewon Phee, Jeroen P. Roose, & Arthur Weiss. (2006). Phosphoinositide-dependent kinase 1 targets protein kinase A in a pathway that regulates interleukin 4. The Journal of Experimental Medicine. 203(7). 1733–1744. 37 indexed citations
14.
Lawrence, Nicola, Peter K. Dearden, David A. Hartley, et al.. (2000). dTcf antagonises Wingless signalling during the development and patterning of the wing in Drosophila. The International Journal of Developmental Biology. 44(7). 749–756. 15 indexed citations
15.
Molenaar, Miranda, et al.. (2000). Differential expression of the Groucho-related genes 4 and 5 during early development of Xenopus laevis. Mechanisms of Development. 91(1-2). 311–315. 43 indexed citations
16.
Cavallo, Rossana, Rachel T. Cox, Jeroen P. Roose, et al.. (1998). Drosophila Tcf and Groucho interact to repress Wingless signalling activity. Nature. 395(6702). 604–608. 587 indexed citations breakdown →
17.
Molenaar, Miranda, et al.. (1998). Differential expression of the HMG box transcription factors XTcf-3 and XLef-1 during early Xenopus development. Mechanisms of Development. 75(1-2). 151–154. 67 indexed citations
18.
Roose, Jeroen P., Miranda Molenaar, Petra Moerer, et al.. (1998). The Xenopus Wnt effector XTcf-3 interacts with Groucho-related transcriptional repressors. Nature. 395(6702). 608–612. 572 indexed citations breakdown →
19.
Korver, Wouter, Jeroen P. Roose, & Hans Clevers. (1997). The winged-helix transcription factor Trident is expressed in cycling cells. Nucleic Acids Research. 25(9). 1715–1719. 208 indexed citations
20.
Molenaar, Miranda, Marc van de Wetering, Mariëtte A. Oosterwegel, et al.. (1996). XTcf-3 Transcription Factor Mediates β-Catenin-Induced Axis Formation in Xenopus Embryos. Cell. 86(3). 391–399. 1571 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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