Davide Ruggero

25.1k total citations · 4 hit papers
91 papers, 11.9k citations indexed

About

Davide Ruggero is a scholar working on Molecular Biology, Immunology and Cell Biology. According to data from OpenAlex, Davide Ruggero has authored 91 papers receiving a total of 11.9k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Molecular Biology, 12 papers in Immunology and 11 papers in Cell Biology. Recurrent topics in Davide Ruggero's work include RNA modifications and cancer (35 papers), RNA and protein synthesis mechanisms (29 papers) and PI3K/AKT/mTOR signaling in cancer (18 papers). Davide Ruggero is often cited by papers focused on RNA modifications and cancer (35 papers), RNA and protein synthesis mechanisms (29 papers) and PI3K/AKT/mTOR signaling in cancer (18 papers). Davide Ruggero collaborates with scholars based in United States, Italy and Canada. Davide Ruggero's co-authors include Pier Paolo Pandolfi, Morgan Truitt, Kevan M. Shokat, Nahum Sonenberg, Morris E. Feldman, Andrew C. Hsieh, Craig R. Stumpf, Eduardo Magalhães Rego, Ornella Zollo and Beth Apsel and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Davide Ruggero

89 papers receiving 11.8k citations

Hit Papers

The translational landsca... 2003 2026 2010 2018 2012 2009 2003 2012 250 500 750

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Davide Ruggero 9.9k 1.7k 1.6k 1.3k 1.1k 91 11.9k
John K. Cowell 7.0k 0.7× 1.6k 0.9× 2.2k 1.4× 1.6k 1.3× 923 0.8× 302 11.3k
Richard B. Pearson 9.6k 1.0× 1.3k 0.7× 2.1k 1.3× 1.5k 1.2× 889 0.8× 136 12.4k
Senji Shirasawa 5.8k 0.6× 2.0k 1.2× 2.7k 1.7× 804 0.6× 920 0.8× 191 9.3k
Bryan A. Ballif 7.8k 0.8× 1.1k 0.6× 1.8k 1.1× 1.8k 1.4× 599 0.5× 104 10.5k
Bruno Amati 9.1k 0.9× 1.4k 0.8× 2.9k 1.8× 940 0.8× 902 0.8× 94 10.7k
Vuk Stambolic 8.6k 0.9× 1.6k 0.9× 2.5k 1.6× 1.1k 0.9× 1.0k 0.9× 85 10.8k
Sheng‐Cai Lin 8.2k 0.8× 1.9k 1.1× 1.3k 0.8× 1.2k 0.9× 1.6k 1.5× 90 11.4k
Roberto D. Polakiewicz 6.7k 0.7× 1.3k 0.7× 1.4k 0.9× 741 0.6× 948 0.9× 69 9.1k
Lihua Julie Zhu 6.8k 0.7× 1.2k 0.7× 790 0.5× 1.4k 1.1× 656 0.6× 165 9.3k
Peter Juo 7.3k 0.7× 1.3k 0.7× 1.4k 0.9× 842 0.7× 1.4k 1.3× 33 8.8k

Countries citing papers authored by Davide Ruggero

Since Specialization
Citations

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

Fields of papers citing papers by Davide Ruggero

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Davide Ruggero

This figure shows the co-authorship network connecting the top 25 collaborators of Davide Ruggero. A scholar is included among the top collaborators of Davide Ruggero 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 Davide Ruggero. Davide Ruggero 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.
Kovalski, Joanna, Grace A. Hernandez, Gilles Rademaker, et al.. (2025). Functional screen identifies RBM42 as a mediator of oncogenic mRNA translation specificity. Nature Cell Biology. 27(3). 518–529. 3 indexed citations
2.
Koumenis, Constantinos, et al.. (2024). miR-217 Regulates Normal and Tumor Cell Fate Following Induction of Endoplasmic Reticulum Stress. Molecular Cancer Research. 22(4). 360–372. 1 indexed citations
3.
Yang, Haojun, Satoshi Oikawa, Juan A. Osés-Prieto, et al.. (2024). Remodelling of the translatome controls diet and its impact on tumorigenesis. Nature. 633(8028). 189–197. 19 indexed citations
4.
Xu, Adele, Zijian Zhang, Kerriann M. Casey, et al.. (2023). Subfunctionalized expression drives evolutionary retention of ribosomal protein paralogs Rps27 and Rps27l in vertebrates. eLife. 12. 9 indexed citations
5.
Kuzuoğlu‐Öztürk, Duygu, Ozlem Aksoy, Christin Schmidt, et al.. (2022). N-myc–Mediated Translation Control Is a Therapeutic Vulnerability in Medulloblastoma. Cancer Research. 83(1). 130–140. 6 indexed citations
6.
Forester, Craig M., Juan A. Osés-Prieto, Nancy J. Phillips, et al.. (2022). Regulation of eIF4E guides a unique translational program to control erythroid maturation. Science Advances. 8(51). eadd3942–eadd3942. 7 indexed citations
7.
Kuzuoğlu‐Öztürk, Duygu, Zhiqiang Hu, Emily N. Devericks, et al.. (2021). Revealing molecular pathways for cancer cell fitness through a genetic screen of the cancer translatome. Cell Reports. 35(13). 109321–109321. 11 indexed citations
8.
Xu, Yichen, Peiwei Huangyang, Ying Wang, et al.. (2021). ERα is an RNA-binding protein sustaining tumor cell survival and drug resistance. Cell. 184(20). 5215–5229.e17. 111 indexed citations
9.
Zhao, Ning, Yangjie Huang, Yung-Hua Wang, et al.. (2020). Ferronostics: Measuring Tumoral Ferrous Iron with PET to Predict Sensitivity to Iron-Targeted Cancer Therapies. Journal of Nuclear Medicine. 62(7). 949–955. 34 indexed citations
10.
Wei, Junnian, Kevin Leung, Charles Truillet, et al.. (2019). Profiling the Surfaceome Identifies Therapeutic Targets for Cells with Hyperactive mTORC1 Signaling. Molecular & Cellular Proteomics. 19(2). 294–307. 9 indexed citations
11.
Fish, Lisa, Albertas Navickas, Bruce Culbertson, et al.. (2019). Nuclear TARBP2 Drives Oncogenic Dysregulation of RNA Splicing and Decay. Molecular Cell. 75(5). 967–981.e9. 60 indexed citations
12.
Nguyen, Hao G., Crystal S. Conn, Lingru Xue, et al.. (2018). Development of a stress response therapy targeting aggressive prostate cancer. Science Translational Medicine. 10(439). 115 indexed citations
13.
Truillet, Charles, John T. Cunningham, Matthew F.L. Parker, et al.. (2016). Noninvasive Measurement of mTORC1 Signaling with 89Zr-Transferrin. Clinical Cancer Research. 23(12). 3045–3052. 20 indexed citations
14.
Galeas, Jacqueline, Morgan Truitt, Cameron Pitt, et al.. (2015). Enhanced MET Translation and Signaling Sustains K-Ras–Driven Proliferation under Anchorage-Independent Growth Conditions. Cancer Research. 75(14). 2851–2862. 38 indexed citations
15.
Pourdehnad, Michael, Morgan Truitt, Imran Siddiqi, et al.. (2013). Myc and mTOR converge on a common node in protein synthesis control that confers synthetic lethality in Myc-driven cancers. Proceedings of the National Academy of Sciences. 110(29). 11988–11993. 204 indexed citations
16.
Upton, John-Paul, Likun Wang, Dan Han, et al.. (2012). IRE1α Cleaves Select microRNAs During ER Stress to Derepress Translation of Proapoptotic Caspase-2. Science. 338(6108). 818–822. 532 indexed citations breakdown →
17.
Bellodi, Cristian, et al.. (2010). Loss of Function of the Tumor Suppressor DKC1 Perturbs p27 Translation Control and Contributes to Pituitary Tumorigenesis. Cancer Research. 70(14). 6026–6035. 132 indexed citations
18.
Peng, Guang, et al.. (2006). Impaired Control of IRES-Mediated Translation in X-Linked Dyskeratosis Congenita. Science. 312(5775). 902–906. 334 indexed citations
19.
Ruggero, Davide, Silvia Grisendi, Francesco Piazza, et al.. (2003). Dyskeratosis Congenita and Cancer in Mice Deficient in Ribosomal RNA Modification. Science. 299(5604). 259–262. 345 indexed citations
20.
Ruggero, Davide. (1993). In vitro translation of archaeal natural mRNAs at high temperature. FEMS Microbiology Letters. 107(1). 89–94.

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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