Mohamed X. Ibrahim

2.7k total citations · 3 hit papers
17 papers, 2.1k citations indexed

About

Mohamed X. Ibrahim is a scholar working on Molecular Biology, Cancer Research and Pathology and Forensic Medicine. According to data from OpenAlex, Mohamed X. Ibrahim has authored 17 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 7 papers in Cancer Research and 1 paper in Pathology and Forensic Medicine. Recurrent topics in Mohamed X. Ibrahim's work include RNA Research and Splicing (5 papers), Cancer, Lipids, and Metabolism (4 papers) and Genomics, phytochemicals, and oxidative stress (3 papers). Mohamed X. Ibrahim is often cited by papers focused on RNA Research and Splicing (5 papers), Cancer, Lipids, and Metabolism (4 papers) and Genomics, phytochemicals, and oxidative stress (3 papers). Mohamed X. Ibrahim collaborates with scholars based in Sweden, United States and China. Mohamed X. Ibrahim's co-authors include Martin O. Bergö, Volkan I. Sayin, Per Lindahl, Jonas A. Nilsson, Erik Larsson, Clotilde Wiel, Kristell Le Gal, Christin Karlsson, Murali K. Akula and Martin G. Dalin and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Mohamed X. Ibrahim

17 papers receiving 2.0k citations

Hit Papers

Antioxidants Accelerate Lung Cancer Progression in Mice 2014 2026 2018 2022 2014 2015 2019 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mohamed X. Ibrahim Sweden 13 1.4k 539 275 215 189 17 2.1k
Numsen Hail United States 29 1.6k 1.2× 326 0.6× 385 1.4× 275 1.3× 171 0.9× 41 2.4k
Clotilde Wiel Sweden 18 1.2k 0.9× 442 0.8× 281 1.0× 239 1.1× 109 0.6× 35 1.9k
Volkan I. Sayin Sweden 17 2.1k 1.5× 862 1.6× 376 1.4× 263 1.2× 264 1.4× 41 3.0k
Dipti Mangal United States 7 1.6k 1.1× 569 1.1× 260 0.9× 129 0.6× 181 1.0× 10 2.1k
Teresa Moliné Spain 19 1.1k 0.8× 334 0.6× 236 0.9× 183 0.9× 109 0.6× 38 1.9k
Dong Joon Kim China 25 1.4k 1.0× 404 0.7× 320 1.2× 191 0.9× 115 0.6× 58 2.0k
Timothy J. Humpton United Kingdom 9 1.8k 1.3× 734 1.4× 454 1.7× 170 0.8× 137 0.7× 14 2.4k
Melba C. Jaramillo United States 16 1.4k 1.0× 303 0.6× 209 0.8× 139 0.6× 161 0.9× 28 2.0k
Renee Risingsong United States 26 2.0k 1.4× 354 0.7× 356 1.3× 219 1.0× 191 1.0× 30 2.5k
Chin‐Wen Chi Taiwan 32 1.5k 1.1× 598 1.1× 509 1.9× 129 0.6× 158 0.8× 80 3.0k

Countries citing papers authored by Mohamed X. Ibrahim

Since Specialization
Citations

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

Fields of papers citing papers by Mohamed X. Ibrahim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohamed X. Ibrahim. 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 Mohamed X. Ibrahim. The network helps show where Mohamed X. Ibrahim may publish in the future.

Co-authorship network of co-authors of Mohamed X. Ibrahim

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

All Works

17 of 17 papers shown
1.
Chen, Xue, Haidong Yao, Muhammad Kashif, et al.. (2021). A small-molecule ICMT inhibitor delays senescence of Hutchinson-Gilford progeria syndrome cells. eLife. 10. 19 indexed citations
2.
Zou, Zhiyuan, Kristell Le Gal, Ahmed E. El Zowalaty, et al.. (2021). Antioxidants Promote Intestinal Tumor Progression in Mice. Antioxidants. 10(2). 241–241. 20 indexed citations
3.
Gal, Kristell Le, Clotilde Wiel, Mohamed X. Ibrahim, et al.. (2021). Mitochondria-Targeted Antioxidants MitoQ and MitoTEMPO Do Not Influence BRAF-Driven Malignant Melanoma and KRAS-Driven Lung Cancer Progression in Mice. Antioxidants. 10(2). 163–163. 19 indexed citations
4.
Karlsson, Christin, Murali K. Akula, Anna Staffas, et al.. (2020). Knockout of the RAS endoprotease RCE1 accelerates myeloid leukemia by downregulating GADD45b. Leukemia. 35(2). 606–609. 5 indexed citations
5.
Yao, Haidong, Xue Chen, Muhammad Kashif, et al.. (2020). Targeting RAS‐converting enzyme 1 overcomes senescence and improves progeria‐like phenotypes of ZMPSTE24 deficiency. Aging Cell. 19(8). e13200–e13200. 5 indexed citations
6.
Wiel, Clotilde, Kristell Le Gal, Mohamed X. Ibrahim, et al.. (2019). BACH1 Stabilization by Antioxidants Stimulates Lung Cancer Metastasis. Cell. 178(2). 330–345.e22. 427 indexed citations breakdown →
7.
Akula, Murali K., Mohamed X. Ibrahim, Omar M. Khan, et al.. (2019). Protein prenylation restrains innate immunity by inhibiting Rac1 effector interactions. Nature Communications. 10(1). 3975–3975. 62 indexed citations
9.
Gal, Kristell Le, Mohamed X. Ibrahim, Clotilde Wiel, et al.. (2015). Antioxidants can increase melanoma metastasis in mice. Science Translational Medicine. 7(308). 308re8–308re8. 493 indexed citations breakdown →
10.
Sayin, Volkan I., Mohamed X. Ibrahim, Erik G. Larsson, et al.. (2014). Abstract 503: Antioxidants markedly accelerate tumor growth and reduce survival in mice with KRAS- and BRAF-induced lung cancer by disrupting the ROS-p53 axis. Cancer Research. 74(19_Supplement). 503–503. 1 indexed citations
11.
Sayin, Volkan I., Mohamed X. Ibrahim, Erik Larsson, et al.. (2014). Antioxidants Accelerate Lung Cancer Progression in Mice. Science Translational Medicine. 6(221). 221ra15–221ra15. 656 indexed citations breakdown →
12.
Sayin, Volkan I., Omar M. Khan, Anna Staffas, et al.. (2014). Loss of One Copy of Zfp148 Reduces Lesional Macrophage Proliferation and Atherosclerosis in Mice by Activating p53. Circulation Research. 115(9). 781–789. 32 indexed citations
13.
Ibrahim, Mohamed X., Volkan I. Sayin, Murali K. Akula, et al.. (2013). Targeting Isoprenylcysteine Methylation Ameliorates Disease in a Mouse Model of Progeria. Science. 340(6138). 1330–1333. 85 indexed citations
14.
Sayin, Volkan I., Mohamed X. Ibrahim, Erik Larsson, et al.. (2013). Zfp148 Deficiency Causes Lung Maturation Defects and Lethality in Newborn Mice That Are Rescued by Deletion of p53 or Antioxidant Treatment. PLoS ONE. 8(2). e55720–e55720. 14 indexed citations
15.
Ibrahim, Mohamed X., Björn Redfors, David Pazooki, et al.. (2012). Targeting filamin A reduces K-RAS–induced lung adenocarcinomas and endothelial response to tumor growth in mice. Molecular Cancer. 11(1). 50–50. 44 indexed citations
16.
Khan, Omar M., Mohamed X. Ibrahim, Ing‐Marie Jonsson, et al.. (2011). Geranylgeranyltransferase type I (GGTase-I) deficiency hyperactivates macrophages and induces erosive arthritis in mice. Journal of Clinical Investigation. 121(2). 628–639. 85 indexed citations
17.
Liu, Meng, Anna-Karin Sjögren, Christin Karlsson, et al.. (2010). Targeting the protein prenyltransferases efficiently reduces tumor development in mice with K-RAS-induced lung cancer. Proceedings of the National Academy of Sciences. 107(14). 6471–6476. 86 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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