Pradyot Dash
- Immunology top 1%
- Immune Cell Function and Interaction 21
- T-cell and B-cell Immunology 16
- interferon and immune responses 5
- Immunotherapy and Immune Responses 5
- Oncology top 5%
- CAR-T cell therapy research 7
- Epidemiology top 5%
- Influenza Virus Research Studies 6
- Respiratory viral infections research 5
- Infectious Diseases top 5%
- Molecular Biology top 10%
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- Diabetes and associated disorders 4
- Co-authors
- Paul G. ThomasPeter C. DohertyCory ReynoldsWalid AwadThirumala‐Devi KannegantiGeoffrey NealeBen YoungbloodYiping Fan
- Cited by
- ImmunologyOncologyEpidemiology
- Partner nations
- United StatesUnited KingdomAustralia
In The Last Decade
Pradyot Dash
32 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 92
- Immunology 2.0k
- Oncology 739
- Epidemiology 776
- Infectious Diseases 353
- Molecular Biology 1.1k
Countries citing papers authored by Pradyot Dash
This map shows the geographic impact of Pradyot Dash'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 Pradyot Dash with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pradyot Dash more than expected).
Fields of papers citing papers by Pradyot Dash
This network shows the impact of papers produced by Pradyot Dash. 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 Pradyot Dash. The network helps show where Pradyot Dash may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pradyot Dash, 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 | 2025 | 0 | |
| 2 | 2024 | 15 | |
| 3 | 2022 | 41 | |
| 4 | 2019 | 33 | |
| 5 | 2018 | 62 | |
| 6 | Metabolic signaling directs the reciprocal lineage decisions of αβ and γδ T cells | 2018 | 14 |
| 7 | De Novo Epigenetic Programs Inhibit PD-1 Blockade-Mediated T Cell Rejuvenationbreakdown → | 2017 | 567 |
| 8 | Quantifiable predictive features define epitope-specific T cell receptor repertoiresbreakdown → | 2017 | 520 |
| 9 | 2016 | 37 | |
| 10 | 2015 | 28 | |
| 11 | 2014 | 18 | |
| 12 | 2012 | 28 | |
| 13 | Paired analysis of TCR alpha and TCR beta chains at the single-cell level in mice | 2011 | 8 |
| 14 | 2011 | 66 | |
| 15 | 2010 | 29 | |
| 16 | 2010 | 159 | |
| 17 | 2007 | 32 | |
| 18 | 2006 | 95 | |
| 19 | Comparative phylogenetic analysis of bluetongue virus based on sequencing of two different regions of L2 gene | 2005 | 0 |
| 20 | 2005 | 163 |
About Pradyot Dash
Pradyot Dash is a scholar working on Immunology, Epidemiology and Agronomy and Crop Science, having authored 35 papers that have together received 3.1k indexed citations. Recurring topics across this work include Immune Cell Function and Interaction (21 papers), T-cell and B-cell Immunology (16 papers), CAR-T cell therapy research (7 papers), Influenza Virus Research Studies (6 papers), interferon and immune responses (5 papers), Respiratory viral infections research (5 papers), Immunotherapy and Immune Responses (5 papers) and Diabetes and associated disorders (4 papers). The work is most often cited by research in Immunology (2.0k citations), Oncology (739 citations) and Epidemiology (776 citations). Pradyot Dash has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Paul G. Thomas, Peter C. Doherty, Cory Reynolds, Walid Awad, Thirumala‐Devi Kanneganti, Geoffrey Neale, Ben Youngblood, Yiping Fan, Ardiana Moustaki and Robert Carter. Their work appears in journals such as Virus Research, The Journal of Immunology, Immunity, Journal of Clinical Investigation and Journal of Virology.
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.