Robert Moot

733 total citations
12 papers, 230 citations indexed

About

Robert Moot is a scholar working on Oncology, Molecular Biology and Genetics. According to data from OpenAlex, Robert Moot has authored 12 papers receiving a total of 230 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Oncology, 6 papers in Molecular Biology and 4 papers in Genetics. Recurrent topics in Robert Moot's work include CAR-T cell therapy research (7 papers), Virus-based gene therapy research (4 papers) and CRISPR and Genetic Engineering (4 papers). Robert Moot is often cited by papers focused on CAR-T cell therapy research (7 papers), Virus-based gene therapy research (4 papers) and CRISPR and Genetic Engineering (4 papers). Robert Moot collaborates with scholars based in United States. Robert Moot's co-authors include Christopher B. Doering, H. Trent Spencer, Sunil S. Raikar, Andrew Fedanov, Christopher D. Porada, Graça Almeida‐Porada, Esmail D. Zanjani, Evan Colletti, Bagirath Gangadharan and Chad Sanada and has published in prestigious journals such as Blood, Cancer Research and Gene Therapy.

In The Last Decade

Robert Moot

12 papers receiving 225 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Robert Moot United States 7 144 102 98 54 53 12 230
Walid Warda France 7 132 0.9× 82 0.8× 42 0.4× 61 1.1× 42 0.8× 8 222
Yaohui Wu China 9 263 1.8× 120 1.2× 80 0.8× 63 1.2× 94 1.8× 26 379
Rebecca Epperly United States 7 171 1.2× 83 0.8× 59 0.6× 72 1.3× 40 0.8× 30 241
Yuehui Lin China 8 250 1.7× 53 0.5× 61 0.6× 57 1.1× 40 0.8× 27 292
Irene García‐Cadenas Spain 7 133 0.9× 64 0.6× 48 0.5× 52 1.0× 103 1.9× 11 217
Nils W. Engel Germany 6 178 1.2× 99 1.0× 63 0.6× 81 1.5× 22 0.4× 14 247
Jessica Hulitt United States 3 82 0.6× 59 0.6× 41 0.4× 79 1.5× 44 0.8× 4 187
Jaquelyn T. Zoine United States 9 203 1.4× 79 0.8× 47 0.5× 116 2.1× 22 0.4× 12 266
Kyogo Suzuki Japan 6 185 1.3× 167 1.6× 44 0.4× 80 1.5× 69 1.3× 15 333

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-authorship network of co-authors of Robert Moot

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

All Works

12 of 12 papers shown
1.
2.
Jacoby, Kyle, Robert Moot, William Lu, et al.. (2019). Abstract 4783: Highly efficient, non-viral precision genome engineering for the generation of personalized neoepitope-specific adoptive T cell therapies. Cancer Research. 79(13_Supplement). 4783–4783. 1 indexed citations
3.
Raikar, Sunil S., et al.. (2017). Engineering CD5-Targeted Chimeric Antigen Receptors and Edited T Cells for the Treatment of T-Cell Leukemia. Blood. 130. 1914–1914. 9 indexed citations
4.
5.
Moot, Robert, et al.. (2016). Genetic engineering of chimeric antigen receptors using lamprey derived variable lymphocyte receptors. Molecular Therapy — Oncolytics. 3. 16026–16026. 27 indexed citations
6.
Tran, Reginald, David R. Myers, Byungwook Ahn, et al.. (2015). Improving Lentiviral Transduction Efficiency with Microfluidic Systems. Blood. 126(23). 4415–4415. 4 indexed citations
7.
Moot, Robert, et al.. (2015). Expanding the Ligand Binding Repertoire of Chimeric Antigen Receptors Using Lamprey Variable Lymphocyte Receptors. Blood. 126(23). 3244–3244. 2 indexed citations
8.
Denning, Gabriela, Robert Moot, Daniel C. Whitehead, et al.. (2014). High-throughput screening identifies compounds that enhance lentiviral transduction. Gene Therapy. 21(12). 1008–1020. 18 indexed citations
9.
Tran, Reginald, Byungwook Ahn, David R. Myers, et al.. (2014). Simplified prototyping of perfusable polystyrene microfluidics. Biomicrofluidics. 8(4). 46501–46501. 8 indexed citations
10.
Porada, Christopher D., Chad Sanada, Evan Colletti, et al.. (2011). Phenotypic correction of hemophilia A in sheep by postnatal intraperitoneal transplantation of FVIII-expressing MSC. Experimental Hematology. 39(12). 1124–1135.e4. 52 indexed citations
11.
Iwakoshi, Neal N., Bagirath Gangadharan, Shawn M. Jobe, et al.. (2010). Functional aspects of factor VIII expression after transplantation of genetically‐modified hematopoietic stem cells for hemophilia A. The Journal of Gene Medicine. 12(4). 333–344. 36 indexed citations
12.
Porada, Christopher D., Chad Sanada, Evan Colletti, et al.. (2010). Phenotypic Correction of Hemophilia A by Postnatal Intraperitoneal Transplantation of FVIII-Expressing MSC. Blood. 116(21). 249–249. 3 indexed citations

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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