Gal Chen

874 citations
13 papers · 506 indexed · 1 hit paper · h-index 7

Gal Chen

11 papers receiving 499 citations

Hit Papers

Integrating Artificial Intelligence and Nanotechnology fo...3242019202620212023100200300

Peers

Gal Chen
Comparison fields: 5 of 108
  • Health Informatics 13
  • Biomaterials 91
  • Biomedical Engineering 234
  • Biophysics 29
  • Molecular Biology 166
Replace Francesca Tatini with:
Francesca Tatini Italy
Diego Morone Switzerland
Jeremy Sauer United States
Samit Shah United States
Alexandra Madeira France
Eric C. Spivey United States
Christian Pritz Austria
Shadi Damanpour United States
Jithesh V. Veetil United States
Gal Chen relative to Francesca Tatini Italy Francesca Tatini's profile →
Citations per field
00.5×3.4×
Francesca Tatini · 1×
Citations per year

Countries citing papers authored by Gal Chen

Since Specialization
Citations

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

Fields of papers citing papers by Gal Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Gal Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Gal Chen Line = papers co-authored together Gal Chen links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 20251
2 20250
3 202311
4 202236
5 202240
6 202212
7 202063
8 20209
9 20202
10 20204
11 20204
12 20200
13
Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicinebreakdown →
2019324

About Gal Chen

Gal Chen is a scholar working on Acoustics and Ultrasonics, Instrumentation, Biophysics, Endocrine and Autonomic Systems and Biomedical Engineering, having authored 13 papers that have together received 506 indexed citations. Recurring topics across this work include Photoreceptor and optogenetics research (2 papers), Photonic and Optical Devices (2 papers), Bacteriophages and microbial interactions (2 papers), Advanced Optical Sensing Technologies (2 papers), Lipid Membrane Structure and Behavior (2 papers), RNA and protein synthesis mechanisms (2 papers), Advanced Fluorescence Microscopy Techniques (2 papers) and Nanoplatforms for cancer theranostics (2 papers). The work is most often cited by research in Health Informatics (13 citations), Biomaterials (91 citations), Biomedical Engineering (234 citations), Biophysics (29 citations) and Molecular Biology (166 citations). Gal Chen has collaborated with scholars based in Israel, Germany and Denmark. Frequent co-authors include Avi Schroeder, Jeny Shklover, Janna Shainsky‐Roitman, Omer Adir, Nitzan Krinsky, Maria Poley, Twan Lammers, Sahar Froim, Maya Kaduri and Yair Shemesh. Their work appears in journals such as Advanced Materials, Optics Express, Journal of Visualized Experiments, Advanced Biology and Nature Communications.

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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