Peter Gallant

4.5k total citations · 1 hit paper
55 papers, 3.3k citations indexed

About

Peter Gallant is a scholar working on Molecular Biology, Cell Biology and Oncology. According to data from OpenAlex, Peter Gallant has authored 55 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 14 papers in Cell Biology and 10 papers in Oncology. Recurrent topics in Peter Gallant's work include Ubiquitin and proteasome pathways (16 papers), Hippo pathway signaling and YAP/TAZ (10 papers) and Cancer-related Molecular Pathways (8 papers). Peter Gallant is often cited by papers focused on Ubiquitin and proteasome pathways (16 papers), Hippo pathway signaling and YAP/TAZ (10 papers) and Cancer-related Molecular Pathways (8 papers). Peter Gallant collaborates with scholars based in Switzerland, Germany and United States. Peter Gallant's co-authors include Laura A. Johnston, Erich A. Nigg, Robert N. Eisenman, Paola Bellosta, David A. Prober, Bruce A. Edgar, Claire de la Cova, Yuzuru Shiio, Pei Cheng and Susan M. Parkhurst and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Peter Gallant

53 papers receiving 3.2k citations

Hit Papers

Drosophila myc Regulates Cellular Growth during Development 1999 2026 2008 2017 1999 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter Gallant Switzerland 27 2.3k 1.0k 649 485 341 55 3.3k
Tohru Itoh Japan 36 2.0k 0.9× 332 0.3× 544 0.8× 361 0.7× 56 0.2× 79 4.1k
Sumire Kobayashi United States 23 3.0k 1.3× 331 0.3× 1.4k 2.1× 1.5k 3.2× 203 0.6× 37 5.5k
Christopher B. Marshall Canada 33 1.7k 0.7× 456 0.4× 203 0.3× 96 0.2× 286 0.8× 64 3.0k
Paul Young Australia 26 2.0k 0.8× 519 0.5× 111 0.2× 167 0.3× 112 0.3× 43 2.7k
Shuji Akiyama Japan 28 1.6k 0.7× 712 0.7× 123 0.2× 271 0.6× 248 0.7× 58 2.9k
Sophie Zinn‐Justin France 34 3.0k 1.3× 319 0.3× 135 0.2× 186 0.4× 141 0.4× 102 3.5k
Shin‐ichi Aota Japan 20 2.8k 1.2× 498 0.5× 255 0.4× 216 0.4× 137 0.4× 23 3.5k
Kevin A. Edwards United States 23 1.9k 0.8× 1.6k 1.5× 161 0.2× 404 0.8× 613 1.8× 37 3.6k
Eli Zamir Germany 20 1.8k 0.8× 3.1k 3.0× 463 0.7× 241 0.5× 177 0.5× 35 4.8k
Atsushi Yamagata Japan 30 1.7k 0.7× 460 0.4× 407 0.6× 285 0.6× 233 0.7× 61 2.2k

Countries citing papers authored by Peter Gallant

Since Specialization
Citations

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

Fields of papers citing papers by Peter Gallant

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Gallant

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Gallant. A scholar is included among the top collaborators of Peter Gallant 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 Peter Gallant. Peter Gallant 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.
Schülein‐Völk, Christina, Giacomo Cossa, Theresa Endres, et al.. (2026). MYC binding to nascent RNA suppresses innate immune signaling by R-loop-derived RNA-DNA hybrids. Cell. 189(5). 1371–1388.e29.
2.
Gallant, Peter, Mareike Müller, Steffi Herold, et al.. (2025). The USP11/TCEAL1 complex promotes transcription elongation to sustain oncogenic gene expression in neuroblastoma. Genes & Development. 39(11-12). 751–769.
3.
Cossa, Giacomo, Sabina Ganskih, Yuanjie Wei, et al.. (2024). Nucleolar detention of NONO shields DNA double-strand breaks from aberrant transcripts. Nucleic Acids Research. 52(6). 3050–3068. 10 indexed citations
4.
Cossa, Giacomo, Christina Schülein‐Völk, Francisco Montesinos, et al.. (2024). PAF1c links S-phase progression to immune evasion and MYC function in pancreatic carcinoma. Nature Communications. 15(1). 1446–1446. 7 indexed citations
5.
Papadopoulos, Dimitrios, Timothy J. Russell, Ivan Mikičić, et al.. (2024). The MYCN oncoprotein is an RNA-binding accessory factor of the nuclear exosome targeting complex. Molecular Cell. 84(11). 2070–2086.e20. 8 indexed citations
6.
Hofstetter, Julia, et al.. (2023). Spt5 interacts genetically with Myc and is limiting for brain tumor growth inDrosophila. Life Science Alliance. 7(1). e202302130–e202302130. 1 indexed citations
7.
Ade, Carsten P., Apoorva Baluapuri, Ursula Eilers, et al.. (2021). MYC- and MIZ1-Dependent Vesicular Transport of Double-Strand RNA Controls Immune Evasion in Pancreatic Ductal Adenocarcinoma. Cancer Research. 81(16). 4242–4256. 19 indexed citations
8.
Endres, Theresa, Jan B. Heidelberger, Apoorva Baluapuri, et al.. (2021). Ubiquitylation of MYC couples transcription elongation with double-strand break repair at active promoters. Molecular Cell. 81(4). 830–844.e13. 24 indexed citations
9.
Otto, Christoph, Stefanie Schmidt, Nicole Müller, et al.. (2019). Targeting bromodomain-containing protein 4 (BRD4) inhibits MYC expression in colorectal cancer cells. Neoplasia. 21(11). 1110–1120. 47 indexed citations
10.
Herr, Winship, et al.. (2010). Drosophila Myc Interacts with Host Cell Factor (dHCF) to Activate Transcription and Control Growth. Journal of Biological Chemistry. 285(51). 39623–39636. 13 indexed citations
11.
Gallant, Peter, et al.. (2009). The conserved Myc box 2 and Myc box 3 regions are important, but not essential, for Myc function in vivo. Gene. 436(1-2). 90–100. 12 indexed citations
12.
Stocker, Hugo & Peter Gallant. (2008). Getting Started. Methods in molecular biology. 420. 27–44. 45 indexed citations
13.
Gallant, Peter, et al.. (2008). Max-independent functions of Myc in Drosophila melanogaster. Nature Genetics. 40(9). 1084–1091. 82 indexed citations
14.
Gallant, Peter. (2007). Control of transcription by Pontin and Reptin. Trends in Cell Biology. 17(4). 187–192. 118 indexed citations
15.
Nigg, Erich A., Peter Gallant, & Wilhelm Krek. (2007). Regulation of p34 cdc2 Protein Kinase Activity by Phosphorylation and Cyclin Binding. Novartis Foundation symposium. 170. 72–96. 7 indexed citations
16.
Gallant, Peter. (2006). Myc/Max/Mad in Invertebrates: The Evolution of the Max Network. Current topics in microbiology and immunology. 302. 235–253. 50 indexed citations
17.
Gallant, Peter. (2005). Protein degradation, signaling, microRNAs and cancer. Genome Biology. 6(4). 316–316. 2 indexed citations
18.
Cova, Claire de la, et al.. (2004). Drosophila Myc Regulates Organ Size by Inducing Cell Competition. Cell. 117(1). 107–116. 491 indexed citations
19.
Johnston, Laura A., David A. Prober, Bruce A. Edgar, Robert N. Eisenman, & Peter Gallant. (1999). Drosophila myc Regulates Cellular Growth during Development. Cell. 98(6). 779–790. 540 indexed citations breakdown →
20.
McArthur, Grant A., Carol D. Laherty, Peter J. Hurlin, et al.. (1998). The Mad Protein Family Links Transcriptional Repression to Cell Differentiation. Cold Spring Harbor Symposia on Quantitative Biology. 63(0). 423–434. 49 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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