Jeffrey D. Varner

2.9k total citations
69 papers, 2.1k citations indexed

About

Jeffrey D. Varner is a scholar working on Molecular Biology, Hematology and Cell Biology. According to data from OpenAlex, Jeffrey D. Varner has authored 69 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Molecular Biology, 15 papers in Hematology and 6 papers in Cell Biology. Recurrent topics in Jeffrey D. Varner's work include Microbial Metabolic Engineering and Bioproduction (21 papers), Gene Regulatory Network Analysis (16 papers) and Retinoids in leukemia and cellular processes (12 papers). Jeffrey D. Varner is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (21 papers), Gene Regulatory Network Analysis (16 papers) and Retinoids in leukemia and cellular processes (12 papers). Jeffrey D. Varner collaborates with scholars based in United States, Switzerland and United Kingdom. Jeffrey D. Varner's co-authors include Anirikh Chakrabarti, Matthew P. DeLisa, Robert Conrado, James E. Bailey, Martin Fussenegger, Doraiswami Ramkrishna, Deyan Luan, Andrew Yen, Kapil Gadkar and Francis J. Doyle and has published in prestigious journals such as Journal of Biological Chemistry, Nature Biotechnology and PLoS ONE.

In The Last Decade

Jeffrey D. Varner

69 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jeffrey D. Varner United States 22 1.5k 286 267 182 168 69 2.1k
Irene M. Ong United States 26 1.4k 0.9× 112 0.4× 199 0.7× 86 0.5× 195 1.2× 88 2.3k
Lingyun Li China 27 1.9k 1.2× 509 1.8× 281 1.1× 52 0.3× 71 0.4× 82 2.4k
Dennis B. Rylatt Australia 21 1.5k 1.0× 309 1.1× 102 0.4× 137 0.8× 122 0.7× 32 2.3k
Dan Lü United States 17 1.1k 0.7× 473 1.7× 255 1.0× 35 0.2× 277 1.6× 36 2.1k
Sebastian Müller Germany 31 2.7k 1.8× 311 1.1× 160 0.6× 186 1.0× 488 2.9× 78 4.1k
Hiroyuki Tsuchiya Japan 31 1.3k 0.8× 178 0.6× 178 0.7× 186 1.0× 471 2.8× 164 3.1k
Alexander S. Hebert United States 35 2.8k 1.9× 268 0.9× 391 1.5× 30 0.2× 173 1.0× 67 4.0k
Alina D. Zamfir Romania 32 1.9k 1.2× 797 2.8× 404 1.5× 94 0.5× 48 0.3× 127 2.8k
Yama Abassi United States 27 1.3k 0.8× 229 0.8× 609 2.3× 39 0.2× 312 1.9× 38 2.6k

Countries citing papers authored by Jeffrey D. Varner

Since Specialization
Citations

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

Fields of papers citing papers by Jeffrey D. Varner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jeffrey D. Varner

This figure shows the co-authorship network connecting the top 25 collaborators of Jeffrey D. Varner. A scholar is included among the top collaborators of Jeffrey D. Varner 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 Jeffrey D. Varner. Jeffrey D. Varner 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.
Feng, Zhihao, Yifan Cheng, Alexandra Khlyustova, et al.. (2023). Virtual High‐Throughput Screening of Vapor‐Deposited Amphiphilic Polymers for Inhibiting Biofilm Formation. Advanced Materials Technologies. 8(13). 11 indexed citations
2.
Williams, Rebecca M., et al.. (2023). Endothelial cells metabolically regulate breast cancer invasion toward a microvessel. APL Bioengineering. 7(4). 46116–46116. 4 indexed citations
3.
Le, Tuan, Lauren T. Moffatt, Thomas Orfeo, et al.. (2023). Comparison of Rapid-, Kaolin-, and Native-TEG Parameters in Burn Patient Cohorts With Acute Burn-induced Coagulopathy and Abnormal Fibrinolytic Function. Journal of Burn Care & Research. 45(1). 70–79. 3 indexed citations
4.
Natarajan, Aravind, Thapakorn Jaroentomeechai, Asif Shajahan, et al.. (2020). Engineering orthogonal human O-linked glycoprotein biosynthesis in bacteria. Nature Chemical Biology. 16(10). 1062–1070. 36 indexed citations
5.
Varner, Jeffrey D., et al.. (2019). Analysis of the role of thrombomodulin in all-trans retinoic acid treatment of coagulation disorders in cancer patients. Theoretical Biology and Medical Modelling. 16(1). 3–3. 7 indexed citations
6.
Dai, David L., et al.. (2019). Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry. Journal of Visualized Experiments. 8 indexed citations
7.
Calhoun, Kara, et al.. (2018). Sequence Specific Modeling of E. coli Cell-Free Protein Synthesis. ACS Synthetic Biology. 7(8). 1844–1857. 26 indexed citations
8.
Shoemaker, Christine A., et al.. (2018). Dynamic Optimization with Particle Swarms (DOPS): a meta-heuristic for parameter estimation in biochemical models. BMC Systems Biology. 12(1). 87–87. 4 indexed citations
9.
Gould, Russell A., et al.. (2016). Population Heterogeneity in the Epithelial to Mesenchymal Transition Is Controlled by NFAT and Phosphorylated Sp1. PLoS Computational Biology. 12(12). e1005251–e1005251. 21 indexed citations
10.
Bunaciu, Rodica P., et al.. (2015). 6-Formylindolo(3,2-b)Carbazole (FICZ) Modulates the Signalsome Responsible for RA-Induced Differentiation of HL-60 Myeloblastic Leukemia Cells. PLoS ONE. 10(8). e0135668–e0135668. 29 indexed citations
11.
Varner, Jeffrey D., et al.. (2015). Dynamic Modeling of the Human Coagulation Cascade Using Reduced Order Effective Kinetic Models. Processes. 3(1). 178–203. 6 indexed citations
12.
Sun, Shan, Igor Titushkin, Jeffrey D. Varner, & Michael Cho. (2012). Millimeter Wave-induced Modulation of Calcium Dynamics in an Engineered Skin Co-culture Model: Role of Secreted ATP on Calcium Spiking. Journal of Radiation Research. 53(2). 159–167. 8 indexed citations
13.
Chakrabarti, Anirikh, Scott S. Verbridge, Abraham D. Stroock, Claudia Fischbach, & Jeffrey D. Varner. (2012). Multiscale Models of Breast Cancer Progression. Annals of Biomedical Engineering. 40(11). 2488–2500. 39 indexed citations
14.
Chakrabarti, Anirikh, et al.. (2011). A review of the mammalian unfolded protein response. Biotechnology and Bioengineering. 108(12). 2777–2793. 332 indexed citations
15.
Luan, Deyan, Fania Szlam, Kenichi A. Tanaka, Philip S. Barie, & Jeffrey D. Varner. (2010). Ensembles of uncertain mathematical models can identify network response to therapeutic interventions. Molecular BioSystems. 6(11). 2272–2286. 23 indexed citations
16.
Tasseff, Ryan, et al.. (2010). Analysis of the Molecular Networks in Androgen Dependent and Independent Prostate Cancer Revealed Fragile and Robust Subsystems. PLoS ONE. 5(1). e8864–e8864. 20 indexed citations
17.
Nayak, Satyaprakash, et al.. (2008). A Test of Highly Optimized Tolerance Reveals Fragile Cell-Cycle Mechanisms Are Molecular Targets in Clinical Cancer Trials. PLoS ONE. 3(4). e2016–e2016. 15 indexed citations
18.
Luan, Deyan, et al.. (2007). Computationally Derived Points of Fragility of a Human Cascade Are Consistent with Current Therapeutic Strategies. PLoS Computational Biology. 3(7). e142–e142. 85 indexed citations
19.
Srivastava, Ranjan & Jeffrey D. Varner. (2007). Emerging Technologies: Systems Biology. Biotechnology Progress. 23(1). 24–27. 6 indexed citations
20.
Gadkar, Kapil, Francis J. Doyle, & Jeffrey D. Varner. (2005). Model identification of signal transduction networks from data using a state regulator problem. PubMed. 2(1). 17–30. 61 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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