Gagan Deep

7.6k total citations · 1 hit paper
136 papers, 6.0k citations indexed

About

Gagan Deep is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Cancer Research. According to data from OpenAlex, Gagan Deep has authored 136 papers receiving a total of 6.0k indexed citations (citations by other indexed papers that have themselves been cited), including 89 papers in Molecular Biology, 53 papers in Pulmonary and Respiratory Medicine and 38 papers in Cancer Research. Recurrent topics in Gagan Deep's work include Silymarin and Mushroom Poisoning (39 papers), Extracellular vesicles in disease (31 papers) and MicroRNA in disease regulation (15 papers). Gagan Deep is often cited by papers focused on Silymarin and Mushroom Poisoning (39 papers), Extracellular vesicles in disease (31 papers) and MicroRNA in disease regulation (15 papers). Gagan Deep collaborates with scholars based in United States, India and Canada. Gagan Deep's co-authors include Rajesh Agarwal, Chapla Agarwal, Umesh C. S. Yadav, Rakesh K. Singh, Vibha Rani, Komaraiah Palle, Ashish Kumar, Rana P. Singh, Gatikrushna Panigrahi and Harold J. Ting and has published in prestigious journals such as Journal of the American Chemical Society, SHILAP Revista de lepidopterología and ACS Nano.

In The Last Decade

Gagan Deep

133 papers receiving 5.9k citations

Hit Papers

Oxidative stress and metabolic disorders: Pathogenesis an... 2016 2026 2019 2022 2016 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
Gagan Deep United States 47 3.3k 1.6k 1.5k 780 728 136 6.0k
Yong Yang China 45 3.3k 1.0× 1.0k 0.6× 1.3k 0.8× 1.0k 1.3× 967 1.3× 130 6.3k
Xiaoguang Chen China 42 4.3k 1.3× 828 0.5× 1.4k 0.9× 899 1.2× 668 0.9× 282 8.3k
Peter Kubatka Slovakia 48 3.7k 1.1× 541 0.3× 1.5k 1.0× 1.0k 1.3× 782 1.1× 200 8.3k
Na Lu China 50 4.3k 1.3× 725 0.4× 1.4k 1.0× 1.5k 1.9× 1.9k 2.6× 199 7.7k
Ying Huang United States 45 4.6k 1.4× 742 0.5× 1.1k 0.8× 1.5k 2.0× 295 0.4× 195 8.3k
Yong‐Yeon Cho South Korea 54 5.1k 1.5× 573 0.4× 1.0k 0.7× 1.5k 1.9× 578 0.8× 268 8.6k
Xi Zheng China 42 2.9k 0.9× 393 0.2× 915 0.6× 561 0.7× 638 0.9× 203 6.5k
Zeping Hu China 42 3.6k 1.1× 429 0.3× 1.5k 1.0× 897 1.1× 281 0.4× 90 6.3k
Lingzhi Wang Singapore 45 3.6k 1.1× 572 0.3× 1.8k 1.2× 1.5k 1.9× 411 0.6× 148 6.4k
Meiyu Geng China 48 5.3k 1.6× 597 0.4× 1.1k 0.7× 1.4k 1.8× 852 1.2× 295 8.7k

Countries citing papers authored by Gagan Deep

Since Specialization
Citations

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

Fields of papers citing papers by Gagan Deep

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gagan Deep

This figure shows the co-authorship network connecting the top 25 collaborators of Gagan Deep. A scholar is included among the top collaborators of Gagan Deep 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 Gagan Deep. Gagan Deep 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.
Kim, Susy, Yixin Su, Sangeeta Singh, et al.. (2025). Bacterial Nanovesicles as Interkingdom Signaling Moieties Mediating Pain Hypersensitivity. ACS Nano. 19(3). 3210–3225. 3 indexed citations
2.
3.
Kumar, Ashish, Susy Kim, Yixin Su, et al.. (2024). Characterisation of LPS+ bacterial extracellular vesicles along the gut‐hepatic portal vein‐liver axis. Journal of Extracellular Vesicles. 13(7). e12474–e12474. 21 indexed citations
4.
Ahmad, Sarfaraz, Gagan Deep, Henry Punzi, et al.. (2024). Chymase Activity in Plasma and Urine Extracellular Vesicles in Primary Hypertension. SHILAP Revista de lepidopterología. 5(11). 1613–1622. 2 indexed citations
5.
Deep, Gagan, et al.. (2023). Comparative study on hermetic and non-hermetic storage of Pearl millet (Pennisetum glaucum). International Journal of Advanced Biochemistry Research. 7(2S). 349–352. 1 indexed citations
6.
Rather, Hilal, Ashish Kumar, Susy Kim, et al.. (2023). The β-Secretase 1 Enzyme as a Novel Therapeutic Target for Prostate Cancer. Cancers. 16(1). 10–10. 2 indexed citations
8.
Kumar, Ashish, Yixin Su, Sangeeta Singh, et al.. (2022). PET imaging of kappa opioid receptors and receptor expression quantified in neuron-derived extracellular vesicles in socially housed female and male cynomolgus macaques. Neuropsychopharmacology. 48(2). 410–417. 9 indexed citations
9.
Gonzales, Mitzi M., Sudarshan Krishnamurthy, Valentina R. Garbarino, et al.. (2021). A geroscience motivated approach to treat Alzheimer’s disease: Senolytics move to clinical trials. Mechanisms of Ageing and Development. 200. 111589–111589. 19 indexed citations
10.
Lifshits, Liubov M., John A. Roque, Prathyusha Konda, et al.. (2020). Near-infrared absorbing Ru(ii) complexes act as immunoprotective photodynamic therapy (PDT) agents against aggressive melanoma. Chemical Science. 11(43). 11740–11762. 92 indexed citations
11.
Kumar, Ashish & Gagan Deep. (2020). Exosomes in hypoxia-induced remodeling of the tumor microenvironment. Cancer Letters. 488. 1–8. 67 indexed citations
12.
Deep, Gagan, et al.. (2018). Novel insecticides: A potential tool for the management of insect pest. Journal of Entomology and Zoology Studies. 6(5). 277–281. 4 indexed citations
13.
O’Bryant, Cindy L., et al.. (2017). Silibinin Treatment Inhibits the Growth of Hedgehog Inhibitor‐Resistant Basal Cell Carcinoma Cells via Targeting EGFR‐MAPK‐Akt and Hedgehog Signaling. Photochemistry and Photobiology. 93(4). 999–1007. 24 indexed citations
14.
Nambiar, Dhanya K., Paulraj Rajamani, Gagan Deep, et al.. (2015). Silibinin Preferentially Radiosensitizes Prostate Cancer by Inhibiting DNA Repair Signaling. Molecular Cancer Therapeutics. 14(12). 2722–2734. 38 indexed citations
15.
Raja, Huzefa A., Tamam El‐Elimat, Mario Figueroa, et al.. (2015). Phylogenetic and chemical diversity of fungal endophytes isolated from Silybum marianum (L) Gaertn. (milk thistle). Mycology: An International Journal on Fungal Biology. 6(1). 8–27. 23 indexed citations
17.
Deep, Gagan, Chapla Agarwal, Michael F. Wempe, et al.. (2014). Silibinin and its 2,3‐dehydro‐derivative inhibit basal cell carcinoma growth via suppression of mitogenic signaling and transcription factors activation. Molecular Carcinogenesis. 55(1). 3–14. 33 indexed citations
18.
Deep, Gagan, Komal Raina, Rana P. Singh, et al.. (2008). Isosilibinin inhibits advanced human prostate cancer growth in athymic nude mice: Comparison with silymarin and silibinin. International Journal of Cancer. 123(12). 2750–2758. 29 indexed citations
19.
Raina, Komal, Marie‐José Blouin, Rana P. Singh, et al.. (2007). Dietary Feeding of Silibinin Inhibits Prostate Tumor Growth and Progression in Transgenic Adenocarcinoma of the Mouse Prostate Model. Cancer Research. 67(22). 11083–11091. 65 indexed citations
20.
Deep, Gagan, Monisha Dhiman, A.R. Rao, & R.K. Kale. (2005). Chemopreventive potential of Triphala (a composite Indian drug) on benzo(a)pyrene induced forestomach tumorigenesis in murine tumor model system.. PubMed. 24(4). 555–63. 46 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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