David J. Wooten

1.1k total citations
24 papers, 664 citations indexed

About

David J. Wooten is a scholar working on Materials Chemistry, Molecular Biology and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, David J. Wooten has authored 24 papers receiving a total of 664 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Materials Chemistry, 7 papers in Molecular Biology and 5 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in David J. Wooten's work include Electronic and Structural Properties of Oxides (7 papers), Computational Drug Discovery Methods (4 papers) and Multiferroics and related materials (4 papers). David J. Wooten is often cited by papers focused on Electronic and Structural Properties of Oxides (7 papers), Computational Drug Discovery Methods (4 papers) and Multiferroics and related materials (4 papers). David J. Wooten collaborates with scholars based in United States, Ukraine and United Kingdom. David J. Wooten's co-authors include Vito Quaranta, Christian T. Meyer, Carlos F. Lopez, Réka Albert, Darren R. Tyson, James C. Petrosky, Joshua A. Bauer, P. A. Dowben, Leonard A. Harris and Keisha N. Hardeman and has published in prestigious journals such as Nature Communications, Bioinformatics and Cancer Research.

In The Last Decade

David J. Wooten

23 papers receiving 658 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David J. Wooten United States 14 282 139 116 106 62 24 664
Shihong Wang China 19 503 1.8× 52 0.4× 51 0.4× 128 1.2× 41 0.7× 69 1.4k
N. Nagasundaram India 18 550 2.0× 112 0.8× 93 0.8× 119 1.1× 50 0.8× 46 1.1k
Yaqin Liu China 17 556 2.0× 98 0.7× 28 0.2× 223 2.1× 12 0.2× 44 1.0k
Zhi Liang China 19 343 1.2× 214 1.5× 52 0.4× 30 0.3× 23 0.4× 37 788
Yonglong Li China 13 317 1.1× 143 1.0× 52 0.4× 32 0.3× 22 0.4× 44 650
Iain B. Styles United Kingdom 21 559 2.0× 63 0.5× 28 0.2× 28 0.3× 21 0.3× 67 1.3k
Lizhe Zhu China 18 615 2.2× 252 1.8× 63 0.5× 61 0.6× 33 0.5× 48 967
Zifu Wang United States 20 965 3.4× 120 0.9× 40 0.3× 249 2.3× 23 0.4× 49 1.4k
Yu-Chen Lin Taiwan 9 319 1.1× 21 0.2× 57 0.5× 14 0.1× 27 0.4× 23 584

Countries citing papers authored by David J. Wooten

Since Specialization
Citations

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

Fields of papers citing papers by David J. Wooten

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David J. Wooten

This figure shows the co-authorship network connecting the top 25 collaborators of David J. Wooten. A scholar is included among the top collaborators of David J. Wooten 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 J. Wooten. David J. Wooten 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.
Wooten, David J., Indu Sinha, & Raghu Sinha. (2022). Selenium Induces Pancreatic Cancer Cell Death Alone and in Combination with Gemcitabine. Biomedicines. 10(1). 149–149. 6 indexed citations
2.
Wooten, David J., Jorge Gómez Tejeda Zañudo, David Murrugarra, et al.. (2021). Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies. PLoS Computational Biology. 17(3). e1008690–e1008690. 11 indexed citations
3.
Wooten, David J., Melat T. Gebru, Hong‐Gang Wang, & Réka Albert. (2021). Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets. Journal of Personalized Medicine. 11(3). 193–193. 8 indexed citations
4.
Wooten, David J., et al.. (2021). MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery. Nature Communications. 12(1). 4607–4607. 59 indexed citations
5.
Wooten, David J. & Réka Albert. (2020). synergy: a Python library for calculating, analyzing and visualizing drug combination synergy. Bioinformatics. 37(10). 1473–1474. 32 indexed citations
6.
Meyer, Christian T., David J. Wooten, Carlos F. Lopez, & Vito Quaranta. (2020). Charting the Fragmented Landscape of Drug Synergy. Trends in Pharmacological Sciences. 41(4). 266–280. 52 indexed citations
7.
Wooten, David J., Sarah M. Groves, Darren R. Tyson, et al.. (2019). Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers. PLoS Computational Biology. 15(10). e1007343–e1007343. 75 indexed citations
8.
Mistry, Akshitkumar M., David J. Wooten, L. Taylor Davis, et al.. (2019). Ventricular-Subventricular Zone Contact by Glioblastoma is Not Associated with Molecular Signatures in Bulk Tumor Data. Scientific Reports. 9(1). 1842–1842. 24 indexed citations
9.
Meyer, Christian T., David J. Wooten, B. Bishal Paudel, et al.. (2019). Quantifying Drug Combination Synergy along Potency and Efficacy Axes. Cell Systems. 8(2). 97–108.e16. 116 indexed citations
10.
Mistry, Akshitkumar M., David J. Wooten, Bret C. Mobley, Vito Quaranta, & Rebecca A. Ihrie. (2018). PATH-34. VENTRICULAR-SUBVENTRICULAR ZONE CONTACT BY GLIOBLASTOMA IS NOT ASSOCIATED WITH MOLECULAR SIGNATURES IN BULK TUMOR DATA. Neuro-Oncology. 20(suppl_6). vi166–vi166.
11.
Wooten, David J. & Vito Quaranta. (2017). Mathematical models of cell phenotype regulation and reprogramming: Make cancer cells sensitive again!. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1867(2). 167–175. 17 indexed citations
12.
Udyavar, Akshata R., David J. Wooten, Mukesh Bansal, et al.. (2016). Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity. Cancer Research. 77(5). 1063–1074. 59 indexed citations
13.
Raj, Himanshu, Stefan Saroiu, Alec Wolman, et al.. (2016). fTPM: A Software-Only Implementation of a {TPM} Chip. USENIX Security Symposium. 841–856. 42 indexed citations
14.
England, P., et al.. (2016). RIoT - A Foundation for Trust in the Internet of Things. 9 indexed citations
15.
Santana, Juan A., Pan Liu, Xianjie Wang, et al.. (2012). The local metallicity of gadolinium doped compound semiconductors. Journal of Physics Condensed Matter. 24(44). 445801–445801. 8 indexed citations
16.
Adamiv, V.T., David J. Wooten, John W. McClory, et al.. (2010). The Electronic Structure and Secondary Pyroelectric Properties of Lithium Tetraborate. Materials. 3(9). 4550–4579. 28 indexed citations
17.
Xiao, Jie, Ya. B. Losovyj, David J. Wooten, et al.. (2010). Surface charging at the (100) surface of Cu doped and undoped Li2B4O7. Applied Surface Science. 257(8). 3399–3403. 11 indexed citations
18.
Wooten, David J., Ihor Ketsman, Jie Xiao, et al.. (2009). Differences in the Surface Charging at the (100) and (110) Surfaces of Li2B4O7. MRS Proceedings. 1164. 8 indexed citations
19.
Losovyj, Ya. B., David J. Wooten, K. D. Belashchenko, et al.. (2009). Comparison of n-type Gd2O3and Gd-doped HfO2. Journal of Physics Condensed Matter. 21(4). 45602–45602. 39 indexed citations
20.
Ketsman, Ihor, David J. Wooten, Jie Xiao, et al.. (2009). The off-axis pyroelectric effect observed for lithium tetraborate. Physics Letters A. 374(6). 891–895. 18 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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