Kenneth G. Geles

1.8k total citations · 1 hit paper
17 papers, 1.4k citations indexed

About

Kenneth G. Geles is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Kenneth G. Geles has authored 17 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 7 papers in Oncology and 4 papers in Cancer Research. Recurrent topics in Kenneth G. Geles's work include Cancer Cells and Metastasis (5 papers), Cancer Genomics and Diagnostics (3 papers) and RNA Research and Splicing (3 papers). Kenneth G. Geles is often cited by papers focused on Cancer Cells and Metastasis (5 papers), Cancer Genomics and Diagnostics (3 papers) and RNA Research and Splicing (3 papers). Kenneth G. Geles collaborates with scholars based in United States, Canada and Japan. Kenneth G. Geles's co-authors include Marc Damelin, Bin‐Bing S. Zhou, Haiying Zhang, Peter B. Dirks, Justin C. Grindley, Robert Tjian, Stephen A. Adam, Ping Hu, Richard N. Freiman and Ronald A. DePinho and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Genes & Development and The EMBO Journal.

In The Last Decade

Kenneth G. Geles

17 papers receiving 1.4k citations

Hit Papers

Tumour-initiating cells: challenges and opportunities for... 2009 2026 2014 2020 2009 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
Kenneth G. Geles United States 11 885 526 264 163 120 17 1.4k
Gary K. Yiu United States 13 990 1.1× 379 0.7× 245 0.9× 252 1.5× 137 1.1× 15 1.5k
Nicolle Besselink Netherlands 17 672 0.8× 350 0.7× 440 1.7× 160 1.0× 59 0.5× 28 1.2k
Holger Hess‐Stumpp Germany 18 973 1.1× 307 0.6× 430 1.6× 136 0.8× 163 1.4× 34 1.5k
Andrea Lunardi United States 20 1.1k 1.3× 511 1.0× 342 1.3× 106 0.7× 186 1.6× 35 1.6k
Apolinar Maya‐Mendoza United Kingdom 23 1.8k 2.0× 631 1.2× 282 1.1× 181 1.1× 151 1.3× 45 2.2k
Larisa Litovchick United States 24 1.4k 1.5× 669 1.3× 245 0.9× 151 0.9× 119 1.0× 54 1.8k
Pascal Drané France 17 1.4k 1.6× 464 0.9× 211 0.8× 176 1.1× 104 0.9× 24 1.7k
Sharmila A. Bapat India 20 1.6k 1.9× 1.5k 2.8× 741 2.8× 127 0.8× 207 1.7× 53 2.5k
Amanda S. Coutts United Kingdom 23 1.1k 1.2× 451 0.9× 219 0.8× 310 1.9× 105 0.9× 44 1.6k
Alberto Martín Spain 12 1.2k 1.4× 840 1.6× 222 0.8× 99 0.6× 131 1.1× 23 1.7k

Countries citing papers authored by Kenneth G. Geles

Since Specialization
Citations

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

Fields of papers citing papers by Kenneth G. Geles

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kenneth G. Geles. 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 Kenneth G. Geles. The network helps show where Kenneth G. Geles may publish in the future.

Co-authorship network of co-authors of Kenneth G. Geles

This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth G. Geles. A scholar is included among the top collaborators of Kenneth G. Geles 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 Kenneth G. Geles. Kenneth G. Geles 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.
Bang, Injin, Takamitsu Hattori, Akiko Koide, et al.. (2024). Selective targeting of oncogenic hotspot mutations of the HER2 extracellular domain. Nature Chemical Biology. 21(5). 706–715. 1 indexed citations
2.
Thomas, Graham D., Luca Micci, Wenjing Yang, et al.. (2021). Intra-Tumoral Activation of Endosomal TLR Pathways Reveals a Distinct Role for TLR3 Agonist Dependent Type-1 Interferons in Shaping the Tumor Immune Microenvironment. Frontiers in Oncology. 11. 711673–711673. 15 indexed citations
3.
Giannakou, Andreas, et al.. (2020). Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression. Journal of Visualized Experiments. 7 indexed citations
4.
Giannakou, Andreas, et al.. (2020). Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression. Journal of Visualized Experiments. 3 indexed citations
5.
Giannakou, Andreas, Jonathon Golas, Wenyan Zhong, et al.. (2018). Cancer-associated fibroblasts suppress SOX2-induced dysplasia in a lung squamous cancer coculture. Proceedings of the National Academy of Sciences. 115(50). E11671–E11680. 54 indexed citations
6.
Geles, Kenneth G., et al.. (2016). Upregulation of RNA Processing Factors in Poorly Differentiated Lung Cancer Cells. Translational Oncology. 9(2). 89–98. 9 indexed citations
7.
Dann, Stephen G., Anthony M. Barsotti, Jonathon Golas, et al.. (2015). Reciprocal regulation of amino acid import and epigenetic state through Lat1 and EZH 2. The EMBO Journal. 34(13). 1773–1785. 49 indexed citations
8.
Geles, Kenneth G., Yijie Gao, Latha Sridharan, et al.. (2015). Abstract 1697: Therapeutic targeting the NOTCH3 receptor with antibody drug conjugates. Cancer Research. 75(15_Supplement). 1697–1697. 5 indexed citations
9.
Bot, Adrian, Maurizio Chiriva‐Internati, Andrew N. Cornforth, et al.. (2014). Stem cells and cancer immunotherapy: Arrowhead’s 2nd annual cancer immunotherapy conference. Journal for ImmunoTherapy of Cancer. 2(1). 2 indexed citations
10.
Sapra, Puja, Marc Damelin, John F. DiJoseph, et al.. (2012). Long-term Tumor Regression Induced by an Antibody–Drug Conjugate That Targets 5T4, an Oncofetal Antigen Expressed on Tumor-Initiating Cells. Molecular Cancer Therapeutics. 12(1). 38–47. 51 indexed citations
11.
Zhou, Bin‐Bing S., Haiying Zhang, Marc Damelin, et al.. (2009). Tumour-initiating cells: challenges and opportunities for anticancer drug discovery. Nature Reviews Drug Discovery. 8(10). 806–823. 687 indexed citations breakdown →
12.
Coleman, Robert A., Patricia Grob, David S. King, et al.. (2008). Structural Changes in TAF4b-TFIID Correlate with Promoter Selectivity. Molecular Cell. 29(1). 81–91. 51 indexed citations
13.
Hu, Ping, Kenneth G. Geles, Ji-Hye Paik, Ronald A. DePinho, & Robert Tjian. (2008). Codependent Activators Direct Myoblast-Specific MyoD Transcription. Developmental Cell. 15(4). 534–546. 123 indexed citations
14.
Geles, Kenneth G., et al.. (2006). Cell-type-selective induction of c-jun by TAF4b directs ovarian-specific transcription networks. Proceedings of the National Academy of Sciences. 103(8). 2594–2599. 30 indexed citations
15.
Freiman, Richard N., Kenneth G. Geles, Kirk Lo, et al.. (2005). Maintenance of spermatogenesis requires TAF4b, a gonad-specific subunit of TFIID. Genes & Development. 19(7). 794–803. 184 indexed citations
16.
Geles, Kenneth G., et al.. (2002). A Role forCaenorhabditis elegansImportin IMA-2 in Germ Line and Embryonic Mitosis. Molecular Biology of the Cell. 13(9). 3138–3147. 42 indexed citations
17.
Geles, Kenneth G. & Stephen A. Adam. (2001). Germline and developmental roles of the nuclear transport factor importin α3 in C. elegans. Development. 128(10). 1817–1830. 78 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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