Gitit Lavy-Shahaf

3.6k citations
18 papers · 223 · h-index 6

Impact in

Papers in

Gitit Lavy-Shahaf

14 papers receiving 217 citations

Peers

Gitit Lavy-Shahaf
Comparison fields: 5 of 51
  • Genetics 115
  • Structural Biology 8
  • Virology 16
  • Cancer Research 35
  • Modeling and Simulation 8
Replace Ian Williamson with:
Ian Williamson United States
Emily N. Pawlak Canada
Matthias Mulazzani Germany
Prospero Civita Italy
Marine Potez Switzerland
Benjamin Dartigues France
Yahaya A Yabo Norway
Gitit Shahaf Israel
Deborah Boyett United States
Pierrick Régnard France
Gitit Lavy-Shahaf relative to Ian Williamson United States Ian Williamson's profile →
Citations per field
00.5×9.6×
Ian Williamson · 1×
Citations per year

Countries citing papers authored by Gitit Lavy-Shahaf

Since Specialization
Citations

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

Fields of papers citing papers by Gitit Lavy-Shahaf

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gitit Lavy-Shahaf. 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 Gitit Lavy-Shahaf. The network helps show where Gitit Lavy-Shahaf may publish in the future.

Co-authors

The 25 scholars most cited alongside Gitit Lavy-Shahaf, 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 Gitit Lavy-Shahaf Line = papers co-authored together Gitit Lavy-Shahaf links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 201991
2 202340
3 202129
4 201426
5 202016
6 20179
7 20174
8 20232
9 20231
10 20181
11 20181
12 20191
13 20181
14 20171
15 20250
16 20230
17 20180
18 20200

About Gitit Lavy-Shahaf

Gitit Lavy-Shahaf is a scholar working on Genetics, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Cancer Research and Molecular Biology, having authored 18 papers that have together received 223 indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (11 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Radiation Therapy and Dosimetry (3 papers), Radiopharmaceutical Chemistry and Applications (3 papers), Cancer Genomics and Diagnostics (2 papers), Medical Imaging Techniques and Applications (2 papers), Advanced Electron Microscopy Techniques and Applications (2 papers) and Photoreceptor and optogenetics research (1 paper). The work is most often cited by research in Genetics (115 citations), Structural Biology (8 citations), Virology (16 citations), Cancer Research (35 citations) and Modeling and Simulation (8 citations). Gitit Lavy-Shahaf has collaborated with scholars based in United States, Switzerland and Czechia. Frequent co-authors include Matthew T. Ballo, Noa Urman, Zéev Bomzon, Steven A. Toms, Jai Grewal, Adrian Kinzel, Patrick R. Conlon, Aaron Rulseh, Josef Vymazal and Alberto Cagigi. Their work appears in journals such as International Journal of Radiation Oncology*Biology*Physics, Neuro-Oncology, Journal of Clinical Pathology, Journal of Clinical Oncology and Clinical Trials.

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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