David Baxter

6.1k total citations · 3 hit papers
43 papers, 4.7k citations indexed

About

David Baxter is a scholar working on Molecular Biology, Cancer Research and Epidemiology. According to data from OpenAlex, David Baxter has authored 43 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 11 papers in Cancer Research and 7 papers in Epidemiology. Recurrent topics in David Baxter's work include MicroRNA in disease regulation (10 papers), Extracellular vesicles in disease (7 papers) and Cancer-related molecular mechanisms research (5 papers). David Baxter is often cited by papers focused on MicroRNA in disease regulation (10 papers), Extracellular vesicles in disease (7 papers) and Cancer-related molecular mechanisms research (5 papers). David Baxter collaborates with scholars based in United States, United Kingdom and Luxembourg. David Baxter's co-authors include Kai Wang, David J. Galas, Jessica A. Weber, Shile Zhang, Ming‐Jen Lee, David Huang, Kuo‐How Huang, Ji‐Hoon Cho, Yue Yuan and Leroy Hood and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Circulation.

In The Last Decade

David Baxter

43 papers receiving 4.6k citations

Hit Papers

The MicroRNA Spectrum in 12 Body Fluids 2010 2026 2015 2020 2010 2010 2012 500 1000 1.5k 2.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Baxter United States 20 3.3k 3.0k 363 304 261 43 4.7k
Juan Liu China 33 2.5k 0.8× 1.5k 0.5× 332 0.9× 837 2.8× 390 1.5× 185 4.7k
Paola de Candia Italy 36 2.2k 0.7× 1.1k 0.4× 258 0.7× 516 1.7× 240 0.9× 68 3.7k
Xiaoming Zhang China 33 1.8k 0.5× 752 0.2× 546 1.5× 297 1.0× 424 1.6× 180 3.8k
Bo Chen China 28 1.6k 0.5× 1.2k 0.4× 294 0.8× 380 1.3× 206 0.8× 190 3.2k
Juan Sandoval Spain 39 3.7k 1.1× 1.1k 0.4× 498 1.4× 471 1.5× 437 1.7× 115 5.5k
Ling Lin China 31 2.3k 0.7× 1.4k 0.5× 248 0.7× 1.2k 4.0× 281 1.1× 108 5.4k
Megan R. Lerner United States 32 2.0k 0.6× 1.2k 0.4× 305 0.8× 388 1.3× 434 1.7× 124 4.0k
Timothy A. McCaffrey United States 35 2.1k 0.6× 915 0.3× 386 1.1× 547 1.8× 373 1.4× 88 4.0k
Xue Yang China 30 2.4k 0.7× 1.5k 0.5× 568 1.6× 466 1.5× 666 2.6× 134 4.6k
Robert L. Medcalf Australia 45 2.0k 0.6× 1.8k 0.6× 454 1.3× 593 2.0× 889 3.4× 169 6.1k

Countries citing papers authored by David Baxter

Since Specialization
Citations

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

Fields of papers citing papers by David Baxter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Baxter

This figure shows the co-authorship network connecting the top 25 collaborators of David Baxter. A scholar is included among the top collaborators of David Baxter 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 David Baxter. David Baxter 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.
Burns, Adam R., Jack Wiedrick, Michal Maes, et al.. (2023). Proteomic changes induced by longevity-promoting interventions in mice. GeroScience. 46(2). 1543–1560. 2 indexed citations
2.
Baxter, David, et al.. (2022). Injuries in Underbody Blast Fatalities: Identification of Five Distinct Mechanisms of Head Injury. Journal of Neurotrauma. 40(1-2). 141–147. 1 indexed citations
3.
Ghai, Vikas, Shannon Fallen, David Baxter, et al.. (2020). Alterations in Plasma microRNA and Protein Levels in War Veterans with Chronic Mild Traumatic Brain Injury. Journal of Neurotrauma. 37(12). 1418–1430. 35 indexed citations
4.
Loux, Shavahn C., Cláudia Barbosa Fernandes, Pouya Dini, et al.. (2019). Small RNA (sRNA) expression in the chorioallantois, endometrium and serum of mares following experimental induction of placentitis. Reproduction Fertility and Development. 31(6). 1144–1156. 11 indexed citations
5.
Ghai, Vikas, David Baxter, Xiaogang Wu, et al.. (2019). Circulating RNAs as predictive markers for the progression of type 2 diabetes. Journal of Cellular and Molecular Medicine. 23(4). 2753–2768. 20 indexed citations
6.
Etheridge, Alton, Kai Wang, David Baxter, & David J. Galas. (2018). Preparation of Small RNA NGS Libraries from Biofluids. Methods in molecular biology. 1740. 163–175. 13 indexed citations
7.
Wu, Xiaogang, Taek‐Kyun Kim, David Baxter, et al.. (2017). sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline. Nucleic Acids Research. 45(21). 12140–12151. 57 indexed citations
8.
Kash, John C., Kathie‐Anne Walters, Jason Kindrachuk, et al.. (2017). Longitudinal peripheral blood transcriptional analysis of a patient with severe Ebola virus disease. Science Translational Medicine. 9(385). 23 indexed citations
9.
Lee, Inyoul, David Baxter, Min Young Lee, Kelsey Scherler, & Kai Wang. (2016). The Importance of Standardization on Analyzing Circulating RNA. Molecular Diagnosis & Therapy. 21(3). 259–268. 48 indexed citations
10.
Anderson, Sarah, Inyoul Lee, Dennis A. Stephenson, et al.. (2014). Disrupted SOX10 function causes spongiform neurodegeneration in gray tremor mice. Mammalian Genome. 26(1-2). 80–93. 3 indexed citations
11.
12.
Baxter, David, David Sharp, Claire Feeney, et al.. (2013). Pituitary dysfunction after blast traumatic brain injury. Annals of Neurology. 74(4). 527–536. 61 indexed citations
13.
Wang, Kai, et al.. (2012). Comparing the MicroRNA Spectrum between Serum and Plasma. PLoS ONE. 7(7). e41561–e41561. 550 indexed citations breakdown →
14.
Wang, Kai, Shile Zhang, Jessica A. Weber, David Baxter, & David J. Galas. (2010). Export of microRNAs and microRNA-protective protein by mammalian cells. Nucleic Acids Research. 38(20). 7248–7259. 833 indexed citations breakdown →
15.
Weber, Jessica A., David Baxter, Shile Zhang, et al.. (2010). The MicroRNA Spectrum in 12 Body Fluids. Clinical Chemistry. 56(11). 1733–1741. 2185 indexed citations breakdown →
16.
Kutlu, Burak, David Baxter, Joanne Rasschaert, et al.. (2009). Detailed transcriptome atlas of the pancreatic beta cell. BMC Medical Genomics. 2(1). 3–3. 95 indexed citations
17.
DiMario, Francis J., Lance O. Bauer, & David Baxter. (1999). Respiratory Sinus Arrhythmia of Brainstem Lesions. Journal of Child Neurology. 14(4). 229–232. 6 indexed citations
18.
Pfeiffer, E., et al.. (1997). Finding and treating depression in Alzheimer's patients: a study of the effects on patients and caregivers.. PubMed. 33(4). 721–9. 10 indexed citations
19.
Blaustein, Alvin, et al.. (1989). Myocardial glutathione depletion impairs recovery after short periods of ischemia.. Circulation. 80(5). 1449–1457. 89 indexed citations
20.
Baxter, David. (1986). A radioreceptor assay for pharmaceutical preparations of insulin. Journal of Biological Standardization. 14(4). 319–330. 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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