Reshma Taneja

5.9k total citations
109 papers, 4.3k citations indexed

About

Reshma Taneja is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, Reshma Taneja has authored 109 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Molecular Biology, 23 papers in Immunology and 16 papers in Cancer Research. Recurrent topics in Reshma Taneja's work include Epigenetics and DNA Methylation (29 papers), Cancer-related gene regulation (13 papers) and Genomics and Chromatin Dynamics (13 papers). Reshma Taneja is often cited by papers focused on Epigenetics and DNA Methylation (29 papers), Cancer-related gene regulation (13 papers) and Genomics and Chromatin Dynamics (13 papers). Reshma Taneja collaborates with scholars based in Singapore, United States and France. Reshma Taneja's co-authors include Hong Sun, Pierre Chambon, Saghi Ghaffari, Vinay Kumar Rao, Narendra Bharathy, Lorraine J. Gudas, Philippe Bouillet, Pierre Chambon, Sameena Azmi and Jin Rong Ow and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Reshma Taneja

106 papers receiving 4.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Reshma Taneja Singapore 38 3.0k 910 675 592 490 109 4.3k
Andrew J. H. Smith United Kingdom 33 3.4k 1.1× 726 0.8× 706 1.0× 457 0.8× 465 0.9× 57 5.7k
Frieder Schwenk Germany 17 2.5k 0.8× 1.0k 1.1× 767 1.1× 298 0.5× 429 0.9× 20 4.1k
Sam John United States 40 5.1k 1.7× 700 0.8× 1.4k 2.1× 543 0.9× 512 1.0× 67 6.6k
Jeffrey L. Stock United States 30 2.3k 0.8× 506 0.6× 656 1.0× 377 0.6× 465 0.9× 41 4.1k
Jorge Laborda Spain 39 2.9k 1.0× 542 0.6× 925 1.4× 509 0.9× 496 1.0× 86 4.4k
Gianluca Canettieri Italy 34 3.4k 1.1× 290 0.3× 636 0.9× 531 0.9× 668 1.4× 76 4.9k
Yoshihiro Takatsu Japan 18 2.0k 0.7× 672 0.7× 351 0.5× 272 0.5× 303 0.6× 23 3.8k
John D. McNeish United States 29 2.2k 0.7× 535 0.6× 590 0.9× 502 0.8× 588 1.2× 37 4.4k
Lili Guo China 27 1.9k 0.6× 1.2k 1.3× 393 0.6× 1.0k 1.7× 1.4k 2.9× 129 4.3k
Alex Yick‐Lun So United States 18 1.4k 0.5× 551 0.6× 568 0.8× 588 1.0× 251 0.5× 20 2.8k

Countries citing papers authored by Reshma Taneja

Since Specialization
Citations

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

Fields of papers citing papers by Reshma Taneja

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Reshma Taneja

This figure shows the co-authorship network connecting the top 25 collaborators of Reshma Taneja. A scholar is included among the top collaborators of Reshma Taneja 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 Reshma Taneja. Reshma Taneja 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.
Hoddinott, Graeme, Heather R. Draper, Suzanne Staples, et al.. (2025). Children’s preferences among six novel moxifloxacin and linezolid-dispersible tablet formulations. PubMed. 2(4). 208–216.
2.
Taneja, Reshma, et al.. (2025). Role of epigenetics in paediatric cancer pathogenesis & drug resistance. British Journal of Cancer. 132(9). 757–769. 1 indexed citations
3.
Ramanathan, Balaji, et al.. (2025). PLA2 driven lipid signaling drives ARMS tumorigenic cell properties. Cell Communication and Signaling. 23(1). 404–404.
4.
Das, Dipanwita, et al.. (2024). Targeting Dysregulated Lipid Metabolism in Cancer with Pharmacological Inhibitors. Cancers. 16(7). 1313–1313. 22 indexed citations
5.
6.
Yi, Yao, Yingying Zeng, Kiyofumi Hamashima, et al.. (2023). Ribosomal proteins regulate 2-cell-stage transcriptome in mouse embryonic stem cells. Stem Cell Reports. 18(2). 463–474. 9 indexed citations
7.
Lee, Hong‐Gyun, Jaewon Yoon, Jahyun Koo, et al.. (2023). Steady-state memory-phenotype conventional CD4+ T cells exacerbate autoimmune neuroinflammation in a bystander manner via the Bhlhe40/GM-CSF axis. Experimental & Molecular Medicine. 55(5). 1033–1045. 7 indexed citations
8.
Bhat, Akshay, Monica Palanichamy Kala, Vinay Kumar Rao, et al.. (2019). Epigenetic Regulation of the PTEN–AKT–RAC1 Axis by G9a Is Critical for Tumor Growth in Alveolar Rhabdomyosarcoma. Cancer Research. 79(9). 2232–2243. 24 indexed citations
9.
Gustafsson, Charlotte, Annika Reinhardt, Noelia A-González, et al.. (2019). Bhlhe40 and Bhlhe41 transcription factors regulate alveolar macrophage self‐renewal and identity. The EMBO Journal. 38(19). e101233–e101233. 65 indexed citations
10.
Jarjour, Nicholas N., Elizabeth A. Schwarzkopf, Tara R. Bradstreet, et al.. (2019). Bhlhe40 mediates tissue-specific control of macrophage proliferation in homeostasis and type 2 immunity. Nature Immunology. 20(6). 687–700. 57 indexed citations
11.
Numata, Akihiko, Hui Si Kwok, Akira Kawasaki, et al.. (2018). The basic helix-loop-helix transcription factor SHARP1 is an oncogenic driver in MLL-AF6 acute myelogenous leukemia. Nature Communications. 9(1). 1622–1622. 21 indexed citations
12.
Lin, Chih‐Chung, Tara R. Bradstreet, Elizabeth A. Schwarzkopf, et al.. (2016). IL-1–induced Bhlhe40 identifies pathogenic T helper cells in a model of autoimmune neuroinflammation. The Journal of Experimental Medicine. 213(2). 251–271. 66 indexed citations
13.
Baier, Paul Christian, Magdalena M. Brzózka, Sven P. Wichert, et al.. (2014). Mice Lacking the Circadian Modulators SHARP1 and SHARP2 Display Altered Sleep and Mixed State Endophenotypes of Psychiatric Disorders. PLoS ONE. 9(10). e110310–e110310. 26 indexed citations
14.
Gopinadhan, Suma, Shilpa Rani Shankar, Li Li, et al.. (2013). Sharp-1 regulates TGF-β signaling and skeletal muscle regeneration. Journal of Cell Science. 127(Pt 3). 599–608. 18 indexed citations
15.
Ling, Belinda Mei Tze, Suma Gopinadhan, Wai Kay Kok, et al.. (2012). G9a mediates Sharp-1–dependent inhibition of skeletal muscle differentiation. Molecular Biology of the Cell. 23(24). 4778–4785. 38 indexed citations
16.
Pervaiz, Shazib, Reshma Taneja, & Saghi Ghaffari. (2009). Oxidative Stress Regulation of Stem and Progenitor Cells. Antioxidants and Redox Signaling. 11(11). 2777–2789. 147 indexed citations
17.
Liu, Jianjun, et al.. (2009). Sharp‐1 modulates the cellular response to DNA damage. FEBS Letters. 584(3). 619–624. 13 indexed citations
18.
Azmi, Sameena, et al.. (2003). mSharp-1/DEC2, a Basic Helix-Loop-Helix Protein Functions as a Transcriptional Repressor of E Box Activity and Stra13 Expression. Journal of Biological Chemistry. 278(22). 20098–20109. 63 indexed citations
19.
Taneja, Reshma, Bernard Thisse, Filippo M. Rijli, et al.. (1996). The Expression Pattern of the Mouse Receptor Tyrosine Kinase Gene MDK1 Is Conserved through Evolution and Requires Hoxa-2 for Rhombomere-Specific Expression in Mouse Embryos. Developmental Biology. 177(2). 397–412. 79 indexed citations
20.

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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