Keesha E. Erickson

469 total citations
18 papers, 315 citations indexed

About

Keesha E. Erickson is a scholar working on Molecular Biology, Genetics and Molecular Medicine. According to data from OpenAlex, Keesha E. Erickson has authored 18 papers receiving a total of 315 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 8 papers in Genetics and 4 papers in Molecular Medicine. Recurrent topics in Keesha E. Erickson's work include Microbial Metabolic Engineering and Bioproduction (6 papers), CRISPR and Genetic Engineering (5 papers) and Bacterial Genetics and Biotechnology (5 papers). Keesha E. Erickson is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (6 papers), CRISPR and Genetic Engineering (5 papers) and Bacterial Genetics and Biotechnology (5 papers). Keesha E. Erickson collaborates with scholars based in United States, Ireland and Luxembourg. Keesha E. Erickson's co-authors include Anushree Chatterjee, Peter B. Otoupal, Thomas R. Aunins, Ryan T. Gill, Richard G. Posner, Boris Ν. Kholodenko, Angela Jones, Louis Stodieck, Oleksii S. Rukhlenko and Shawn Levy and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Frontiers in Microbiology.

In The Last Decade

Keesha E. Erickson

18 papers receiving 310 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Keesha E. Erickson United States 11 192 76 65 43 39 18 315
Thomas R. Aunins United States 8 124 0.6× 34 0.4× 71 1.1× 40 0.9× 16 0.4× 9 245
Yoshimasa Ishizaki Japan 9 179 0.9× 30 0.4× 25 0.4× 29 0.7× 39 1.0× 14 338
Priyanka Baloni United States 11 208 1.1× 34 0.4× 42 0.6× 24 0.6× 37 0.9× 29 400
Gerda Szakonyi United Kingdom 6 240 1.3× 115 1.5× 7 0.1× 55 1.3× 46 1.2× 7 370
A. Fang United States 10 120 0.6× 12 0.2× 202 3.1× 16 0.4× 4 0.1× 15 361
Natsuko Yamamoto Japan 9 327 1.7× 182 2.4× 6 0.1× 41 1.0× 48 1.2× 14 437
Francesca Bernardini United Kingdom 12 201 1.0× 13 0.2× 49 0.8× 23 0.5× 92 2.4× 18 349
Huiying Zou China 13 241 1.3× 104 1.4× 17 0.3× 32 0.7× 1 0.0× 35 513
Ekaterina E. Zheleznova United States 8 225 1.2× 94 1.2× 18 0.3× 12 0.3× 105 2.7× 9 376

Countries citing papers authored by Keesha E. Erickson

Since Specialization
Citations

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

Fields of papers citing papers by Keesha E. Erickson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Keesha E. Erickson. 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 Keesha E. Erickson. The network helps show where Keesha E. Erickson may publish in the future.

Co-authorship network of co-authors of Keesha E. Erickson

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

All Works

18 of 18 papers shown
1.
Aunins, Thomas R., Colleen M. Courtney, Peter B. Otoupal, et al.. (2021). Facile accelerated specific therapeutic (FAST) platform develops antisense therapies to counter multidrug-resistant bacteria. Communications Biology. 4(1). 331–331. 15 indexed citations
2.
Otoupal, Peter B., et al.. (2021). Potentiating antibiotic efficacy via perturbation of non-essential gene expression. Communications Biology. 4(1). 1267–1267. 14 indexed citations
3.
Aunins, Thomas R., Keesha E. Erickson, & Anushree Chatterjee. (2020). Transcriptome-based design of antisense inhibitors potentiates carbapenem efficacy in CRE Escherichia coli. Proceedings of the National Academy of Sciences. 117(48). 30699–30709. 22 indexed citations
4.
Hlavacek, William S., et al.. (2019). A Step-by-Step Guide to Using BioNetFit. Methods in molecular biology. 1945. 391–419. 2 indexed citations
5.
Erickson, Keesha E., Oleksii S. Rukhlenko, Yen Ting Lin, et al.. (2019). Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor. PLoS Computational Biology. 15(1). e1006706–e1006706. 7 indexed citations
6.
Erickson, Keesha E., Oleksii S. Rukhlenko, Richard G. Posner, William S. Hlavacek, & Boris Ν. Kholodenko. (2018). New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling. Seminars in Cancer Biology. 54. 162–173. 14 indexed citations
7.
Aunins, Thomas R., Keesha E. Erickson, Nripesh Prasad, et al.. (2018). Spaceflight Modifies Escherichia coli Gene Expression in Response to Antibiotic Exposure and Reveals Role of Oxidative Stress Response. Frontiers in Microbiology. 9. 310–310. 75 indexed citations
8.
Rukhlenko, Oleksii S., Aleksandar Krstić, Leonidas G. Alexopoulos, et al.. (2018). Dissecting RAF Inhibitor Resistance by Structure-based Modeling Reveals Ways to Overcome Oncogenic RAS Signaling. Cell Systems. 7(2). 161–179.e14. 50 indexed citations
9.
Erickson, Keesha E., Peter B. Otoupal, & Anushree Chatterjee. (2017). Transcriptome-Level Signatures in Gene Expression and Gene Expression Variability during Bacterial Adaptive Evolution. mSphere. 2(1). 27 indexed citations
10.
Erickson, Keesha E., Nancy Madinger, & Anushree Chatterjee. (2017). Draft Genome Sequences of Clinical Isolates of Multidrug-Resistant Acinetobacter baumannii. Genome Announcements. 5(5). 2 indexed citations
11.
Erickson, Keesha E., et al.. (2017). The Tolerome: A Database of Transcriptome-Level Contributions to Diverse Escherichia coli Resistance and Tolerance Phenotypes. ACS Synthetic Biology. 6(12). 2302–2315. 10 indexed citations
12.
Courtney, Colleen M., Keesha E. Erickson, Lisa M. Wolfe, et al.. (2017). ROS mediated selection for increased NADPH availability in Escherichia coli. Biotechnology and Bioengineering. 114(11). 2685–2689. 12 indexed citations
13.
Erickson, Keesha E., Nancy Madinger, & Anushree Chatterjee. (2016). Draft Genome Sequence for a Clinical Isolate of Vancomycin-Resistant Enterococcus faecalis. Genome Announcements. 4(3). 3 indexed citations
14.
Winkler, James D., Andrea L. Halweg‐Edwards, Keesha E. Erickson, et al.. (2016). The Resistome: A Comprehensive Database of Escherichia coli Resistance Phenotypes. ACS Synthetic Biology. 5(12). 1566–1577. 11 indexed citations
15.
Otoupal, Peter B., Keesha E. Erickson, Antoni E. Bordoy, & Anushree Chatterjee. (2016). CRISPR Perturbation of Gene Expression Alters Bacterial Fitness under Stress and Reveals Underlying Epistatic Constraints. ACS Synthetic Biology. 6(1). 94–107. 20 indexed citations
16.
Winkler, James D., Keesha E. Erickson, Alaksh Choudhury, Andrea L. Halweg‐Edwards, & Ryan T. Gill. (2015). Complex systems in metabolic engineering. Current Opinion in Biotechnology. 36. 107–114. 10 indexed citations
17.
Erickson, Keesha E., Peter B. Otoupal, & Anushree Chatterjee. (2015). Gene Expression Variability Underlies Adaptive Resistance in Phenotypically Heterogeneous Bacterial Populations. ACS Infectious Diseases. 1(11). 555–567. 15 indexed citations
18.
Erickson, Keesha E., Ryan T. Gill, & Anushree Chatterjee. (2014). CONSTRICTOR: Constraint Modification Provides Insight into Design of Biochemical Networks. PLoS ONE. 9(11). e113820–e113820. 6 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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