Jake M. Peterson

433 total citations
12 papers, 240 citations indexed

About

Jake M. Peterson is a scholar working on Molecular Biology, Epidemiology and Infectious Diseases. According to data from OpenAlex, Jake M. Peterson has authored 12 papers receiving a total of 240 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 4 papers in Epidemiology and 3 papers in Infectious Diseases. Recurrent topics in Jake M. Peterson's work include RNA and protein synthesis mechanisms (9 papers), Influenza Virus Research Studies (4 papers) and RNA modifications and cancer (4 papers). Jake M. Peterson is often cited by papers focused on RNA and protein synthesis mechanisms (9 papers), Influenza Virus Research Studies (4 papers) and RNA modifications and cancer (4 papers). Jake M. Peterson collaborates with scholars based in United States and Poland. Jake M. Peterson's co-authors include Walter N. Moss, Collin A. O’Leary, Ryan J. Andrews, Matthew D. Disney, Hafeez S. Haniff, Xiaohui Liu, Jonathan L. Chen, Blessy M. Suresh, Raphael I. Benhamou and Yuquan Tong and has published in prestigious journals such as Journal of Biological Chemistry, PLoS ONE and Biochemistry.

In The Last Decade

Jake M. Peterson

11 papers receiving 240 citations

Peers

Jake M. Peterson
Collin A. O’Leary United States
Shailee Arya United States
Chloe M. Ghent United States
Daniel Michałowski United States
Jailson Brito Querido United States
Collin A. O’Leary United States
Jake M. Peterson
Citations per year, relative to Jake M. Peterson Jake M. Peterson (= 1×) peers Collin A. O’Leary

Countries citing papers authored by Jake M. Peterson

Since Specialization
Citations

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

Fields of papers citing papers by Jake M. Peterson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jake M. Peterson. 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 Jake M. Peterson. The network helps show where Jake M. Peterson may publish in the future.

Co-authorship network of co-authors of Jake M. Peterson

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

All Works

12 of 12 papers shown
1.
Ruszkowska, Agnieszka, Ryszard Kierzek, Paulina Jackowiak, et al.. (2024). Targeting sgRNA N secondary structure as a way of inhibiting SARS-CoV-2 replication. Antiviral Research. 228. 105946–105946. 2 indexed citations
2.
Tompkins, Van S., et al.. (2024). Identification of MYC intron 2 regions that modulate expression. PLoS ONE. 19(1). e0296889–e0296889.
3.
Peterson, Jake M., et al.. (2024). Structure of the SARS-CoV-2 Frameshift Stimulatory Element with an Upstream Multibranch Loop. Biochemistry. 63(10). 1287–1296. 8 indexed citations
4.
Ruszkowska, Agnieszka, Jake M. Peterson, Walter N. Moss, et al.. (2023). In vivo secondary structural analysis of Influenza A virus genomic RNA. Cellular and Molecular Life Sciences. 80(5). 136–136. 11 indexed citations
5.
Peterson, Jake M., et al.. (2023). Discovery of RNA secondary structural motifs using sequence-ordered thermodynamic stability and comparative sequence analysis. MethodsX. 11. 102275–102275. 1 indexed citations
6.
Kierzek, Ryszard, Paweł Zmora, Jake M. Peterson, et al.. (2022). Secondary Structure of Influenza A Virus Genomic Segment 8 RNA Folded in a Cellular Environment. International Journal of Molecular Sciences. 23(5). 2452–2452. 4 indexed citations
7.
Peterson, Jake M., Collin A. O’Leary, & Walter N. Moss. (2022). In silico analysis of local RNA secondary structure in influenza virus A, B and C finds evidence of widespread ordered stability but little evidence of significant covariation. Scientific Reports. 12(1). 310–310. 7 indexed citations
8.
Andrews, Ryan J., Collin A. O’Leary, Van S. Tompkins, et al.. (2021). A map of the SARS-CoV-2 RNA structurome. NAR Genomics and Bioinformatics. 3(2). lqab043–lqab043. 44 indexed citations
9.
Peterson, Jake M., et al.. (2021). Universal and strain specific structure features of segment 8 genomic RNA of influenza A virus—application of 4-thiouridine photocrosslinking. Journal of Biological Chemistry. 297(6). 101245–101245. 10 indexed citations
10.
Peterson, Jake M., et al.. (2021). Thermodynamic stability of hnRNP A1 low complexity domain revealed by high‐pressure NMR. Proteins Structure Function and Bioinformatics. 89(7). 781–791. 4 indexed citations
11.
Haniff, Hafeez S., Yuquan Tong, Xiaohui Liu, et al.. (2020). Targeting the SARS-CoV-2 RNA Genome with Small Molecule Binders and Ribonuclease Targeting Chimera (RIBOTAC) Degraders. ACS Central Science. 6(10). 1713–1721. 144 indexed citations
12.
Prak, Dianne J. Luning, Katherine C. Gordon, Jake M. Peterson, & Daniel W. O’Sullivan. (2012). Photolysis of dinitrobenzyl alcohols, dinitrobenzaldehydes, and nitrobenzoic acids in seawater, estuary water, and pure water. Marine Chemistry. 145-147. 29–36. 5 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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