Deeksha Deep
Impact in
- Immunology top 5%
- Immunotherapy and Immune Responses
- Immune Cell Function and Interaction
- T-cell and B-cell Immunology
- Immune cells in cancer
- Transplantation top 10%
Papers in ⓘ
-
- T-cell and B-cell Immunology 4
- Immune Cell Function and Interaction 4
- Toxin Mechanisms and Immunotoxins 1
- Co-authors
- Alexander Y. Rudensky (3 shared papers)Linas Mažutis (1 shared paper)Yuri Pritykin (1 shared paper)Christina S. Leslie (1 shared paper)Charlotte E. Ariyan (1 shared paper)Dana Pe’er (2 shared papers)Herman Gudjonson (2 shared papers)Vincent‐Philippe Lavallée (1 shared paper)
- Journals
- Cell (3 papers)The Journal of Experimental Medicine (1 paper)Science Translational Medicine (1 paper)Clinical & Experimental Immunology (1 paper)ACS Chemical Biology (1 paper)
- Partner nations
- United StatesJapanUnited Kingdom
In The Last Decade
Deeksha Deep
9 papers receiving 707 citations
Hit Papers
Peers
Comparison fields: 5 of 82
- Immunology 464
- Transplantation 29
- Oncology 138
- Immunology and Allergy 27
- Microbiology 21
Countries citing papers authored by Deeksha Deep
This map shows the geographic impact of Deeksha Deep'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 Deeksha Deep with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deeksha Deep more than expected).
Fields of papers citing papers by Deeksha Deep
This network shows the impact of papers produced by Deeksha Deep. 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 Deeksha Deep. The network helps show where Deeksha Deep may publish in the future.
Co-authors
The 25 scholars most cited alongside Deeksha Deep, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Transcriptional Basis of Mouse and Human Dendritic Cell Heterogeneity Hit paper breakdown → | 2019 | 374 |
| 2 | 2017 | 112 | |
| 3 | 2021 | 90 | |
| 4 | 2017 | 63 | |
| 5 | 2015 | 30 | |
| 6 | 2015 | 22 | |
| 7 | 2016 | 16 | |
| 8 | 2024 | 3 | |
| 9 | 2015 | 3 |
About Deeksha Deep
Deeksha Deep is a scholar working on Developmental Neuroscience, Immunology, Infectious Diseases, Ecology and Biomaterials, having authored 9 papers that have together received 713 indexed citations. Recurring topics across this work include T-cell and B-cell Immunology (4 papers), Immune Cell Function and Interaction (4 papers), Antimicrobial Resistance in Staphylococcus (3 papers), Biochemical and Structural Characterization (3 papers), Bacteriophages and microbial interactions (2 papers), CAR-T cell therapy research (2 papers), Single-cell and spatial transcriptomics (1 paper) and Toxin Mechanisms and Immunotoxins (1 paper). The work is most often cited by research in Immunology (464 citations), Transplantation (29 citations), Oncology (138 citations), Immunology and Allergy (27 citations) and Microbiology (21 citations). Deeksha Deep has collaborated with scholars based in United States, Japan and United Kingdom. Frequent co-authors include Alexander Y. Rudensky, Linas Mažutis, Yuri Pritykin, Christina S. Leslie, Charlotte E. Ariyan, Dana Pe’er, Herman Gudjonson, Vincent‐Philippe Lavallée, Chrysothemis C. Brown and Alejandra Mendoza. Their work appears in journals such as Cell, The Journal of Experimental Medicine, Science Translational Medicine, Clinical & Experimental Immunology and ACS Chemical Biology.
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.