Veena Krishnamoorthy

1.1k total citations
11 papers, 889 citations indexed

About

Veena Krishnamoorthy is a scholar working on Immunology, Molecular Biology and Oncology. According to data from OpenAlex, Veena Krishnamoorthy has authored 11 papers receiving a total of 889 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Immunology, 4 papers in Molecular Biology and 4 papers in Oncology. Recurrent topics in Veena Krishnamoorthy's work include T-cell and B-cell Immunology (5 papers), Acute Lymphoblastic Leukemia research (4 papers) and Immune Cell Function and Interaction (4 papers). Veena Krishnamoorthy is often cited by papers focused on T-cell and B-cell Immunology (5 papers), Acute Lymphoblastic Leukemia research (4 papers) and Immune Cell Function and Interaction (4 papers). Veena Krishnamoorthy collaborates with scholars based in United States and Sweden. Veena Krishnamoorthy's co-authors include Michelle A. Kelliher, Jennifer A. Calvo, Vishva M. Sharma, Kyle Draheim, Nicole Hermance, Kenneth J. Oestreich, Sarah Gilbertson, Kenneth P. Hough, Paul McDonald and Kaitlin A. Read and has published in prestigious journals such as The Journal of Experimental Medicine, Blood and Immunity.

In The Last Decade

Veena Krishnamoorthy

11 papers receiving 881 citations

Peers

Veena Krishnamoorthy
Kiyokazu Kakugawa Japan
Cyril Šálek Czechia
Toru Ikeda Japan
Piotr Grabarczyk Germany
Arianne Pérez-García United States
CJ Sherr United States
Yixiao Gong United States
Alena Malyukova Sweden
Laki Buluwela United Kingdom
Deborah Clarke United Kingdom
Kiyokazu Kakugawa Japan
Veena Krishnamoorthy
Citations per year, relative to Veena Krishnamoorthy Veena Krishnamoorthy (= 1×) peers Kiyokazu Kakugawa

Countries citing papers authored by Veena Krishnamoorthy

Since Specialization
Citations

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

Fields of papers citing papers by Veena Krishnamoorthy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Veena Krishnamoorthy

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

All Works

11 of 11 papers shown
1.
Yeung, Yik A., Veena Krishnamoorthy, Cesar Sommer, et al.. (2020). An Optimized Full-Length FLT3/CD3 Bispecific Antibody Demonstrates Potent Anti-leukemia Activity and Reversible Hematological Toxicity. Molecular Therapy. 28(3). 889–900. 26 indexed citations
2.
Djuretic, Ivana M., Veena Krishnamoorthy, Cesar Sommer, et al.. (2018). A Novel Full Length Anti-FLT3 CD3 Bispecific Antibody for the Treatment of Acute Myeloid Leukemia. Blood. 132(Supplement 1). 1445–1445. 1 indexed citations
3.
Krishnamoorthy, Veena, Sunil Kannanganat, Mark Maienschein‐Cline, et al.. (2017). The IRF4 Gene Regulatory Module Functions as a Read-Write Integrator to Dynamically Coordinate T Helper Cell Fate. Immunity. 47(3). 481–497.e7. 93 indexed citations
4.
Krishnamoorthy, Veena, et al.. (2015). Repression of Ccr9 Transcription in Mouse T Lymphocyte Progenitors by the Notch Signaling Pathway. The Journal of Immunology. 194(7). 3191–3200. 17 indexed citations
5.
Carr, Tiffany, Veena Krishnamoorthy, Shuyang Yu, et al.. (2015). The transcription factor lymphoid enhancer factor 1 controls invariant natural killer T cell expansion and Th2-type effector differentiation. The Journal of Experimental Medicine. 212(5). 793–807. 47 indexed citations
6.
Oestreich, Kenneth J., Kaitlin A. Read, Sarah Gilbertson, et al.. (2014). Bcl-6 directly represses the gene program of the glycolysis pathway. Nature Immunology. 15(10). 957–964. 157 indexed citations
7.
Verykokakis, Mihalis, Veena Krishnamoorthy, Antonio Iavarone, et al.. (2013). Essential Functions for ID Proteins at Multiple Checkpoints in Invariant NKT Cell Development. The Journal of Immunology. 191(12). 5973–5983. 50 indexed citations
8.
Cullion, Kathleen, Kyle Draheim, Nicole Hermance, et al.. (2009). Targeting the Notch1 and mTOR pathways in a mouse T-ALL model. Blood. 113(24). 6172–6181. 105 indexed citations
9.
Sharma, Vishva M., Jennifer A. Calvo, Kyle Draheim, et al.. (2006). Notch1 Contributes to Mouse T-Cell Leukemia by Directly Inducing the Expression of c-myc. Molecular and Cellular Biology. 26(21). 8022–8031. 209 indexed citations
10.
O’Neil, Jennifer, Jennifer A. Calvo, Keith McKenna, et al.. (2005). Activating Notch1 mutations in mouse models of T-ALL. Blood. 107(2). 781–785. 177 indexed citations
11.
O’Neil, Jennifer, Jennifer A. Calvo, Keith McKenna, et al.. (2005). Activating Notch1 Mutations in Mouse Models of T-ALL.. Blood. 106(11). 2609–2609. 7 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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