Ramanuj DasGupta

9.7k total citations · 2 hit papers
75 papers, 6.2k citations indexed

About

Ramanuj DasGupta is a scholar working on Molecular Biology, Oncology and Cell Biology. According to data from OpenAlex, Ramanuj DasGupta has authored 75 papers receiving a total of 6.2k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Molecular Biology, 13 papers in Oncology and 10 papers in Cell Biology. Recurrent topics in Ramanuj DasGupta's work include Wnt/β-catenin signaling in development and cancer (22 papers), Cancer-related gene regulation (14 papers) and RNA Research and Splicing (7 papers). Ramanuj DasGupta is often cited by papers focused on Wnt/β-catenin signaling in development and cancer (22 papers), Cancer-related gene regulation (14 papers) and RNA Research and Splicing (7 papers). Ramanuj DasGupta collaborates with scholars based in United States, Singapore and Australia. Ramanuj DasGupta's co-authors include Elaine Fuchs, Uri Gat, Linda Degenstein, Colin Jamora, Norbert Perrimon, Bradley J. Merrill, Paweł Kocieniewski, Ajamete Kaykas, Randall T. Moon and E. Fuchs and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Ramanuj DasGupta

74 papers receiving 6.1k citations

Hit Papers

De Novo Hair Follicle Morphogenesis and Hair Tumors in Mi... 1998 2026 2007 2016 1998 1999 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ramanuj DasGupta United States 31 4.7k 1.4k 1.2k 684 640 75 6.2k
Tudorita Tumbar United States 25 3.0k 0.6× 1.2k 0.9× 1.3k 1.1× 892 1.3× 303 0.5× 41 5.2k
Thomas Andl United States 36 4.6k 1.0× 1.6k 1.2× 2.2k 1.8× 1.3k 1.8× 1.2k 1.9× 83 7.5k
Maria Kasper Sweden 31 4.2k 0.9× 868 0.6× 1.0k 0.8× 1.2k 1.8× 709 1.1× 67 6.6k
Pritinder Kaur Australia 35 2.0k 0.4× 1.5k 1.1× 726 0.6× 924 1.4× 469 0.7× 66 5.1k
Géraldine Guasch United States 23 2.4k 0.5× 915 0.7× 975 0.8× 897 1.3× 277 0.4× 39 4.6k
Lisa Polak United States 31 3.6k 0.8× 1.9k 1.3× 2.4k 2.0× 1.1k 1.7× 546 0.9× 36 6.8k
Valentina Greco United States 28 2.7k 0.6× 1.6k 1.2× 1.7k 1.4× 997 1.5× 302 0.5× 57 5.2k
Michael Rendl United States 36 4.0k 0.8× 2.1k 1.5× 3.2k 2.6× 896 1.3× 412 0.6× 46 7.4k
M. Peter Marinkovich United States 50 2.5k 0.5× 3.3k 2.4× 604 0.5× 737 1.1× 691 1.1× 131 7.9k
Joerg Huelsken Switzerland 36 6.1k 1.3× 1.3k 1.0× 875 0.7× 2.2k 3.2× 996 1.6× 63 9.0k

Countries citing papers authored by Ramanuj DasGupta

Since Specialization
Citations

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

Fields of papers citing papers by Ramanuj DasGupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ramanuj DasGupta

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

All Works

20 of 20 papers shown
1.
Gultekin, Okan, Shruti Khare, Shiyong Neo, et al.. (2025). Adaptive NK Cells Exhibit Tumor-Specific Immune Memory and Cytotoxicity in Ovarian Cancer. Cancer Immunology Research. 13(7). 1080–1097. 5 indexed citations
2.
3.
Ghoshdastider, Umesh, Neha Rohatgi, Marjan Mojtabavi Naeini, et al.. (2021). Pan-Cancer Analysis of Ligand–Receptor Cross-talk in the Tumor Microenvironment. Cancer Research. 81(7). 1802–1812. 48 indexed citations
4.
Wu, Bangyuan, Li Ren Kong, Chin Wen Png, et al.. (2021). DUSP16 promotes cancer chemoresistance through regulation of mitochondria-mediated cell death. Nature Communications. 12(1). 2284–2284. 48 indexed citations
5.
Low, Joo-Leng, Gokce Oguz, Xiaoqian Zhang, et al.. (2021). Molecular docking-aided identification of small molecule inhibitors targeting β-catenin-TCF4 interaction. iScience. 24(6). 102544–102544. 9 indexed citations
6.
Ravasio, Andrea, Shumei Chia, Aditya Arora, et al.. (2020). Author Correction: Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity. Communications Biology. 3(1). 504–504. 1 indexed citations
7.
Ravasio, Andrea, Shumei Chia, Aditya Arora, et al.. (2020). Author Correction: Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity. Communications Biology. 3(1). 1 indexed citations
8.
Ravasio, Andrea, Shumei Chia, Aditya Arora, et al.. (2020). Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity. Communications Biology. 3(1). 429–429. 5 indexed citations
9.
Arora, Aditya, Jorge Luis Galeano Niño, Shumei Chia, et al.. (2020). Two high-yield complementary methods to sort cell populations by their 2D or 3D migration speed. Molecular Biology of the Cell. 31(25). 2779–2790. 2 indexed citations
10.
Sharma, Ankur & Ramanuj DasGupta. (2019). Tracking tumor evolution one-cell-at-a-time. Molecular & Cellular Oncology. 6(3). 1590089–1590089. 4 indexed citations
11.
Krishna, Srikar, Daniel Yim, Vairavan Lakshmanan, et al.. (2019). Dynamic expression of tRNA‐derived small RNAs define cellular states. EMBO Reports. 20(7). e47789–e47789. 116 indexed citations
12.
Ong, Louis Jun Ye, Terry Ching, Huan Li, et al.. (2019). Self-aligning Tetris-Like (TILE) modular microfluidic platform for mimicking multi-organ interactions. Lab on a Chip. 19(13). 2178–2191. 67 indexed citations
13.
Zhao, Xiaodan, Roland Iványi-Nagy, Clarinda Chua, et al.. (2019). The chromatin structuring protein HMGA2 influences human subtelomere stability and cancer chemosensitivity. PLoS ONE. 14(5). e0215696–e0215696. 16 indexed citations
14.
Sharma, Ankur, Elaine Yiqun Cao, Vibhor Kumar, et al.. (2018). Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy. Nature Communications. 9(1). 4931–4931. 103 indexed citations
15.
Chatterjee, Sujash S., et al.. (2016). TCF7L1 Modulates Colorectal Cancer Growth by Inhibiting Expression of the Tumor-Suppressor Gene EPHB3. Scientific Reports. 6(1). 28299–28299. 37 indexed citations
16.
Lam, Anna P., Jose D. Herazo‐Maya, Joseph A. Sennello, et al.. (2014). Wnt Coreceptor Lrp5 is a Driver of Idiopathic Pulmonary Fibrosis. American Journal of Respiratory and Critical Care Medicine. 190(2). 185–195. 93 indexed citations
17.
DasGupta, Ramanuj, et al.. (2011). Postgenomic technologies targeting the Wnt signaling network. WIREs Systems Biology and Medicine. 3(6). 649–665. 2 indexed citations
18.
DasGupta, Ramanuj & Foster C. Gonsalves. (2008). High-Throughput RNAi Screen in Drosophila. Methods in molecular biology. 469. 163–184. 8 indexed citations
19.
Hayward, Penny, Keith Brennan, Phil Sanders, et al.. (2005). Notch modulates Wnt signalling by associating with Armadillo/β-catenin and regulating its transcriptional activity. Development. 132(8). 1819–1830. 167 indexed citations
20.
DasGupta, Ramanuj, Ajamete Kaykas, Randall T. Moon, & Norbert Perrimon. (2005). Functional Genomic Analysis of the Wnt-Wingless Signaling Pathway. Science. 308(5723). 826–833. 271 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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