Jesse D. Riordan

1.0k total citations
25 papers, 720 citations indexed

About

Jesse D. Riordan is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Jesse D. Riordan has authored 25 papers receiving a total of 720 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 6 papers in Genetics and 5 papers in Cancer Research. Recurrent topics in Jesse D. Riordan's work include RNA modifications and cancer (5 papers), CRISPR and Genetic Engineering (5 papers) and Melanoma and MAPK Pathways (4 papers). Jesse D. Riordan is often cited by papers focused on RNA modifications and cancer (5 papers), CRISPR and Genetic Engineering (5 papers) and Melanoma and MAPK Pathways (4 papers). Jesse D. Riordan collaborates with scholars based in United States, Hong Kong and Spain. Jesse D. Riordan's co-authors include Joseph H. Nadeau, Adam J. Dupuy, David A. Largaespada, Vincent W. Keng, Barbara R. Tschida, Michael Icardi, D. Thomas Rutkowski, Angela M. Arensdorf, Lewis R. Roberts and Todd E. Scheetz and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Investigation and Blood.

In The Last Decade

Jesse D. Riordan

25 papers receiving 716 citations

Peers

Jesse D. Riordan

EPIDE

GENET

Iset Vera United States

Lisa K. Mullany United States

Laura Antonucci Italy

Yunhai Luo China

Marie-Pierre Lambert France

Qi-Heng Yang United States

Andreas I. Papadakis Canada

Donglai Wang China

Mingsong Wang China

Xiuzhi Duan China

Iset Vera United States

Jesse D. Riordan

470 ×1.0

332 ×1.9

121 ×1.0

EPIDE

82 ×0.8

GENET

104 ×1.2

Citations per year, relative to Jesse D. Riordan Jesse D. Riordan (= 1×) peers Iset Vera

Countries citing papers authored by Jesse D. Riordan

Since Specialization

Citations

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

Fields of papers citing papers by Jesse D. Riordan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jesse D. Riordan. 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 Jesse D. Riordan. The network helps show where Jesse D. Riordan may publish in the future.

Co-authorship network of co-authors of Jesse D. Riordan

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

All Works

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20 of 20 papers shown

Stipp, Christopher S., et al.. (2024). Single-cell genomics analysis reveals complex genetic interactions in an in vivo model of acquired BRAF inhibitor resistance. NAR Cancer. 6(1). zcad061–zcad061. 1 indexed citations

Riordan, Jesse D., et al.. (2023). Understanding cancer drug resistance with Sleeping Beauty functional genomic screens: Application to MAPK inhibition in cutaneous melanoma. iScience. 26(10). 107805–107805. 1 indexed citations

Riordan, Jesse D., et al.. (2022). SRC-RAC1 signaling drives drug resistance to BRAF inhibition in de-differentiated cutaneous melanomas. npj Precision Oncology. 6(1). 74–74. 8 indexed citations

Blanc, Valérie, Jesse D. Riordan, Saeed Soleymanjahi, et al.. (2020). Apobec1 complementation factor overexpression promotes hepatic steatosis, fibrosis, and hepatocellular cancer. Journal of Clinical Investigation. 131(1). 36 indexed citations

Voigt, Andrew P., et al.. (2019). Src-Dependent DBL Family Members Drive Resistance to Vemurafenib in Human Melanoma. Cancer Research. 79(19). 5074–5087. 15 indexed citations

Voigt, Andrew P., et al.. (2019). A simplified transposon mutagenesis method to perform phenotypic forward genetic screens in cultured cells. BMC Genomics. 20(1). 497–497. 4 indexed citations

Blanc, Valérie, Yan Xie, Susan Kennedy, et al.. (2018). Apobec1 complementation factor (A1CF) and RBM47 interact in tissue-specific regulation of C to U RNA editing in mouse intestine and liver. RNA. 25(1). 70–81. 33 indexed citations

Ziyad, Safiyyah, Jesse D. Riordan, Trent Su, et al.. (2018). A Forward Genetic Screen Targeting the Endothelium Reveals a Regulatory Role for the Lipid Kinase Pi4ka in Myelo- and Erythropoiesis. Cell Reports. 22(5). 1211–1224. 14 indexed citations

Tschida, Barbara R., Nuri A. Temiz, Jesse D. Riordan, et al.. (2017). Sleeping Beauty Insertional Mutagenesis in Mice Identifies Drivers of Steatosis-Associated Hepatic Tumors. Cancer Research. 77(23). 6576–6588. 34 indexed citations

10.

Riordan, Jesse D. & Joseph H. Nadeau. (2017). From Peas to Disease: Modifier Genes, Network Resilience, and the Genetics of Health. The American Journal of Human Genetics. 101(2). 177–191. 96 indexed citations

11.

Riordan, Jesse D., et al.. (2015). A Hybrid Adenoviral Vector System Achieves Efficient Long-Term Gene Expression in the Liver via piggyBac Transposition. Human Gene Therapy. 26(6). 377–385. 12 indexed citations

12.

Sinasac, David S., et al.. (2015). Genetic control of obesity, glucose homeostasis, dyslipidemia and fatty liver in a mouse model of diet-induced metabolic syndrome. International Journal of Obesity. 40(2). 346–355. 21 indexed citations

13.

Temiz, Nuri A., Branden S. Moriarity, Natalie K. Wolf, et al.. (2015). RNA sequencing of Sleeping Beauty transposon-induced tumors detects transposon-RNA fusions in forward genetic cancer screens. Genome Research. 26(1). 119–129. 23 indexed citations

14.

Riordan, Jesse D. & Joseph H. Nadeau. (2014). Modeling progressive non-alcoholic fatty liver disease in the laboratory mouse. Mammalian Genome. 25(9-10). 473–486. 30 indexed citations

15.

Riordan, Jesse D., Luke Drury, Benjamin T. Brett, et al.. (2014). Sequencing methods and datasets to improve functional interpretation of sleeping beauty mutagenesis screens. BMC Genomics. 15(1). 1150–1150. 19 indexed citations

16.

Rogers, Laura M., Jesse D. Riordan, Brian L. Swick, David K. Meyerholz, & Adam J. Dupuy. (2013). Ectopic Expression of Zmiz1 Induces Cutaneous Squamous Cell Malignancies in a Mouse Model of Cancer. Journal of Investigative Dermatology. 133(7). 1863–1869. 20 indexed citations

17.

Riordan, Jesse D. & Adam J. Dupuy. (2013). Domesticated transposable element gene products in human cancer. Mobile Genetic Elements. 3(5). e26693–e26693. 11 indexed citations

18.

Riordan, Jesse D., Vincent W. Keng, Barbara R. Tschida, et al.. (2013). Identification of Rtl1, a Retrotransposon-Derived Imprinted Gene, as a Novel Driver of Hepatocarcinogenesis. PLoS Genetics. 9(4). e1003441–e1003441. 70 indexed citations

19.

Lim, Lionel, Asha Balakrishnan, Noelle E. Huskey, et al.. (2013). MicroRNA-494 within an oncogenic microRNA megacluster regulates G1/S transition in liver tumorigenesis through suppression of mutated in colorectal cancer. Hepatology. 59(1). 202–215. 92 indexed citations

20.

Keng, Vincent W., Daniela Sia, Aaron L. Sarver, et al.. (2012). Sex bias occurrence of hepatocellular carcinoma in Poly7 molecular subclass is associated with EGFR. Hepatology. 57(1). 120–130. 47 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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