Maya Gavrieli

3.2k total citations · 2 hit papers
7 papers, 2.6k citations indexed

About

Maya Gavrieli is a scholar working on Immunology, Oncology and Molecular Biology. According to data from OpenAlex, Maya Gavrieli has authored 7 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Immunology, 3 papers in Oncology and 2 papers in Molecular Biology. Recurrent topics in Maya Gavrieli's work include T-cell and B-cell Immunology (6 papers), Immune Cell Function and Interaction (6 papers) and Protein Tyrosine Phosphatases (2 papers). Maya Gavrieli is often cited by papers focused on T-cell and B-cell Immunology (6 papers), Immune Cell Function and Interaction (6 papers) and Protein Tyrosine Phosphatases (2 papers). Maya Gavrieli collaborates with scholars based in United States, Italy and Germany. Maya Gavrieli's co-authors include Kenneth M. Murphy, Laurie E. Harrington, Casey T. Weaver, Paul R. Mangan, Theresa L. Murphy, John R. Šedý, Michelle A. Hurchla, Susan K. Loftin, Norihiko Watanabe and Jianfei Yang and has published in prestigious journals such as Immunity, Nature Immunology and The Journal of Immunology.

In The Last Decade

Maya Gavrieli

7 papers receiving 2.6k citations

Hit Papers

Th17: An Effector CD4 T Cell Lineage with Regulatory T Ce... 2003 2026 2010 2018 2006 2003 250 500 750 1000

Peers

Maya Gavrieli
Comparison fields: 5 of 89
  • Immunology 2.0k
  • Oncology 750
  • Molecular Biology 353
  • Epidemiology 326
  • Genetics 178
Replace David Sehy with:
David Sehy United States
Thomas Duhen United States
Amala Alag United States
Lars Ohl Germany
John R. Šedý United States
Roman Krzysiek France
Alina C. Boesteanu United States
Brian M. Macduff Canada
J M Wang Italy
Robert Kastelein United States
David Sehy United States View profile →
Citations per field, relative to Maya Gavrieli
Maya Gavrieli · 1×
Citations per year, relative to Maya Gavrieli
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Countries citing papers authored by Maya Gavrieli

Since Specialization
Citations

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

Fields of papers citing papers by Maya Gavrieli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maya Gavrieli

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

All Works

7 of 7 papers shown
# Work Indexed citations
1 34
2
Th17: An Effector CD4 T Cell Lineage with Regulatory T Cell Ties breakdown →
1103
3 65
4 41
5 499
6 152
7
BTLA is a lymphocyte inhibitory receptor with similarities to CTLA-4 and PD-1 breakdown →
695

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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