Janet Layne

877 total citations · 1 hit paper
9 papers, 726 citations indexed

About

Janet Layne is a scholar working on Biomedical Engineering, Materials Chemistry and Artificial Intelligence. According to data from OpenAlex, Janet Layne has authored 9 papers receiving a total of 726 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Biomedical Engineering, 4 papers in Materials Chemistry and 3 papers in Artificial Intelligence. Recurrent topics in Janet Layne's work include Nanoparticles: synthesis and applications (3 papers), Nanoplatforms for cancer theranostics (3 papers) and Advanced Graph Neural Networks (2 papers). Janet Layne is often cited by papers focused on Nanoparticles: synthesis and applications (3 papers), Nanoplatforms for cancer theranostics (3 papers) and Advanced Graph Neural Networks (2 papers). Janet Layne collaborates with scholars based in United States, Italy and India. Janet Layne's co-authors include Alex Punnoose, Denise Wingett, K. M. Reddy, Kevin Feris, D. A. Ténné, Chongmin Wang, Hua Wang, Jason R. Bell, Mark Engelhard and Edoardo Serra and has published in prestigious journals such as Nanotechnology, Journal of Materials Science Materials in Medicine and Proceedings of the VLDB Endowment.

In The Last Decade

Janet Layne

9 papers receiving 715 citations

Hit Papers

Preferential killing of cancer cells and activated human ... 2008 2026 2014 2020 2008 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Janet Layne United States 6 507 239 183 100 59 9 726
Lukáš Malina Czechia 12 301 0.6× 268 1.1× 79 0.4× 98 1.0× 39 0.7× 33 602
Rahman S. Zabibah Iraq 15 162 0.3× 158 0.7× 67 0.4× 191 1.9× 21 0.4× 111 802
Cao Chen China 5 167 0.3× 156 0.7× 86 0.5× 60 0.6× 13 0.2× 7 404
Eunsu Kim South Korea 12 287 0.6× 310 1.3× 75 0.4× 152 1.5× 32 0.5× 28 650
Daniel Rosenkranz Germany 8 181 0.4× 203 0.8× 70 0.4× 114 1.1× 21 0.4× 11 591
Rhenz Alfred D. Liman Philippines 4 138 0.3× 161 0.7× 144 0.8× 106 1.1× 21 0.4× 4 485
Sajini D. Hettiarachchi United States 13 802 1.6× 449 1.9× 221 1.2× 285 2.9× 37 0.6× 17 1.2k
Surya P. Singh India 9 223 0.4× 175 0.7× 155 0.8× 139 1.4× 3 0.1× 16 652
Pranav Tiwari India 16 779 1.5× 225 0.9× 60 0.3× 233 2.3× 59 1.0× 39 1000
Roger M. Leblanc United States 10 1.0k 2.0× 360 1.5× 123 0.7× 283 2.8× 59 1.0× 11 1.3k

Countries citing papers authored by Janet Layne

Since Specialization
Citations

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

Fields of papers citing papers by Janet Layne

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Janet Layne

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

All Works

9 of 9 papers shown
1.
Layne, Janet, et al.. (2024). Analyzing Robustness of Automatic Scientific Claim Verification Tools against Adversarial Rephrasing Attacks. ACM Transactions on Intelligent Systems and Technology. 15(5). 1–32. 2 indexed citations
2.
Layne, Janet, et al.. (2023). Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs. Proceedings of the VLDB Endowment. 16(9). 2075–2089. 9 indexed citations
3.
Layne, Janet, et al.. (2021). Identifying ATT&CK Tactics in Android Malware Control Flow Graph Through Graph Representation Learning and Interpretability. 2021 IEEE International Conference on Big Data (Big Data). 5602–5608. 10 indexed citations
4.
Campedelli, Gian Maria, et al.. (2021). The geometrical shapes of violence: predicting and explaining terrorist operations through graph embeddings. Journal of Complex Networks. 10(2). 1 indexed citations
5.
Layne, Janet, et al.. (2021). Detecting Botnet Nodes via Structural Node Representation Learning. 2021 IEEE International Conference on Big Data (Big Data). 9. 5357–5364. 7 indexed citations
6.
Thurber, Aaron, Denise Wingett, Janet Layne, et al.. (2011). Improving the selective cancer killing ability of ZnO nanoparticles using Fe doping. Nanotoxicology. 6(4). 440–452. 36 indexed citations
7.
Layne, Janet. (2011). ZINC OXIDE NANOPARTICLES AS POTENTIAL NOVEL ANTICANCER THERAPIES. Scholar Works (Boise State University). 1 indexed citations
8.
Wang, Hua, Denise Wingett, Mark Engelhard, et al.. (2008). Fluorescent dye encapsulated ZnO particles with cell-specific toxicity for potential use in biomedical applications. Journal of Materials Science Materials in Medicine. 20(1). 11–22. 110 indexed citations
9.
Layne, Janet, et al.. (2008). Preferential killing of cancer cells and activated human T cells using ZnO nanoparticles. Nanotechnology. 19(29). 295103–295103. 550 indexed citations breakdown →

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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