Robert Moot

795 citations
12 papers · 580 · 1 hit paper · h-index 9

Impact in

  • Oncology top 10%
    • CAR-T cell therapy research
    • Cancer Immunotherapy and Biomarkers
    • Immunotherapy and Immune Responses
    • Immune Cell Function and Interaction
    • T-cell and B-cell Immunology

Papers in

Robert Moot

12 papers receiving 559 citations

Robert Moot's Hit Papers

Non-viral precision T cell receptor replacement for personalized cell therapy 2022 · 212 citations
2120+1+2Years since publication50100150200

Peers

Robert Moot
Comparison fields: 5 of 49
  • Oncology 355
  • Immunology 201
  • Hematology 57
  • Genetics 131
  • Molecular Biology 228
Replace Daniel M. Schreeder with:
Daniel M. Schreeder United States
Chenggong Li China
Semjon Manuel Willier Germany
Antonio Valeri Spain
Leah Gehrs United States
Trisha R. Berger United States
Kendall J. Schick United States
S B Ebeling Netherlands
Diogo Gomes‐Silva United States
Deanna M. Langfitt United States
Robert Moot relative to Daniel M. Schreeder United States Daniel M. Schreeder's profile →
Citations per field
00.5×1.5×1.8×
Daniel M. Schreeder · 1×
Citations per year

Countries citing papers authored by Robert Moot

Since Specialization
Citations

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

Fields of papers citing papers by Robert Moot

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Robert Moot, 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 Robert Moot Line = papers co-authored together Robert Moot links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1
Non-viral precision T cell receptor replacement for personalized cell therapy
Hit paper breakdown →
2022212
2 2023124
3 201774
4 201154
5 201038
6 201629
7 201418
8 201410
9 201710
10 20155
11 20104
12 20152

About Robert Moot

Robert Moot is a scholar working on Oncology, Hematology, Immunology, Molecular Biology and Genetics, having authored 12 papers that have together received 580 indexed citations. Recurring topics across this work include CAR-T cell therapy research (6 papers), CRISPR and Genetic Engineering (4 papers), Hemophilia Treatment and Research (3 papers), Immunotherapy and Immune Responses (2 papers), Virus-based gene therapy research (2 papers), Immune Cell Function and Interaction (2 papers), Microfluidic and Bio-sensing Technologies (1 paper) and Silicon Carbide Semiconductor Technologies (1 paper). The work is most often cited by research in Oncology (355 citations), Immunology (201 citations), Hematology (57 citations), Genetics (131 citations) and Molecular Biology (228 citations). Robert Moot has collaborated with scholars based in United States. Frequent co-authors include H. Trent Spencer, Christopher B. Doering, Sunil S. Raikar, Andrew Fedanov, Christopher D. Porada, Graça D. Almeida-Porada, Esmail D. Zanjani, Neal N. Iwakoshi, Shawn M. Jobe and Evan Joseph Colletti. Their work appears in journals such as Blood, Nature, The Journal of Gene Medicine, Gene Therapy and OncoImmunology.

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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