Debipriya Das

625 total citations
9 papers, 488 citations indexed

About

Debipriya Das is a scholar working on Molecular Biology, Pathology and Forensic Medicine and Infectious Diseases. According to data from OpenAlex, Debipriya Das has authored 9 papers receiving a total of 488 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 2 papers in Pathology and Forensic Medicine and 1 paper in Infectious Diseases. Recurrent topics in Debipriya Das's work include TGF-β signaling in diseases (7 papers), Renal and related cancers (2 papers) and Genetic factors in colorectal cancer (2 papers). Debipriya Das is often cited by papers focused on TGF-β signaling in diseases (7 papers), Renal and related cancers (2 papers) and Genetic factors in colorectal cancer (2 papers). Debipriya Das collaborates with scholars based in United Kingdom, Germany and France. Debipriya Das's co-authors include Caroline S. Hill, Michael Howell, Laurence Lévy, Pedro Vizán, Vasso Episkopou, Probir Chakravarty, Anassuya Ramachandran, Katherine W. Rogers, J. Vogt and Andrew P. Hinck and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Molecular and Cellular Biology and Cancer Research.

In The Last Decade

Debipriya Das

9 papers receiving 479 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Debipriya Das United Kingdom	7	397	108	46	46	44	9	488
Frederick D. Park United States	5	227 0.6×	82 0.8×	38 0.8×	50 1.1×	27 0.6×	5	349
Kazunobu Isogaya Japan	12	427 1.1×	180 1.7×	34 0.7×	98 2.1×	34 0.8×	15	533
Anna Wiles New Zealand	13	266 0.7×	130 1.2×	24 0.5×	76 1.7×	42 1.0×	17	437
Shunlin Jiang United States	10	349 0.9×	104 1.0×	54 1.2×	137 3.0×	59 1.3×	14	499
James M. Simone United States	9	309 0.8×	43 0.4×	30 0.7×	35 0.8×	79 1.8×	11	476
Shilpa Rao United States	7	351 0.9×	57 0.5×	60 1.3×	97 2.1×	57 1.3×	12	549
Hiroko Fujimoto Japan	12	379 1.0×	225 2.1×	64 1.4×	103 2.2×	78 1.8×	17	617
Marie-Thérèse Webster United Kingdom	5	295 0.7×	82 0.8×	49 1.1×	32 0.7×	41 0.9×	7	377
K. Tago Japan	9	422 1.1×	58 0.5×	35 0.8×	36 0.8×	21 0.5×	16	559
Christina Y. Lee United States	11	160 0.4×	210 1.9×	62 1.3×	53 1.2×	38 0.9×	28	339

Countries citing papers authored by Debipriya Das

Since Specialization

Citations

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

Fields of papers citing papers by Debipriya Das

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Debipriya Das

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

All Works

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9 of 9 papers shown

Russell, Emma, Ana Agua‐Doce, Dina Levi, et al.. (2020). Adapting to the Coronavirus Pandemic: Building and Incorporating a Diagnostic Pipeline in a Shared Resource Laboratory. Cytometry Part A. 99(1). 90–99. 2 indexed citations

Miller, Daniel S. J., Ming Jiang, Ilaria Gori, et al.. (2018). The Dynamics of TGF-β Signaling Are Dictated by Receptor Trafficking via the ESCRT Machinery. Cell Reports. 25(7). 1841–1855.e5. 26 indexed citations

Ramachandran, Anassuya, Pedro Vizán, Debipriya Das, et al.. (2018). TGF-β uses a novel mode of receptor activation to phosphorylate SMAD1/5 and induce epithelial-to-mesenchymal transition. eLife. 7. 163 indexed citations

Briones‐Orta, Marco A., Laurence Lévy, Chris D. Madsen, et al.. (2013). Arkadia Regulates Tumor Metastasis by Modulation of the TGF-β Pathway. Cancer Research. 73(6). 1800–1810. 30 indexed citations

Vizán, Pedro, Daniel S. J. Miller, Ilaria Gori, et al.. (2013). Controlling Long-Term Signaling: Receptor Dynamics Determine Attenuation and Refractory Behavior of the TGF-β Pathway. Science Signaling. 6(305). ra106–ra106. 56 indexed citations

Das, Debipriya, Rebecca A. Randall, & Caroline S. Hill. (2008). An N-terminally truncated Smad2 protein can partially compensate for loss of full-length Smad2. Biochemical Journal. 417(1). 205–212. 2 indexed citations

Lévy, Laurence, et al.. (2007). Arkadia Activates Smad3/Smad4-Dependent Transcription by Triggering Signal-Induced SnoN Degradation. Molecular and Cellular Biology. 27(17). 6068–6083. 143 indexed citations

Biondi, Christine, Debipriya Das, Michael Howell, et al.. (2007). Mice develop normally in the absence of Smad4 nucleocytoplasmic shuttling. Biochemical Journal. 404(2). 235–245. 15 indexed citations

Das, Debipriya, et al.. (2002). DT40 cells lacking the Ca ²⁺ -binding protein annexin 5 are resistant to Ca ²⁺ -dependent apoptosis. Proceedings of the National Academy of Sciences. 99(12). 8054–8059. 51 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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