Subramanian Venkatesan

3.5k total citations
22 papers, 650 citations indexed

About

Subramanian Venkatesan is a scholar working on Cancer Research, Molecular Biology and Oncology. According to data from OpenAlex, Subramanian Venkatesan has authored 22 papers receiving a total of 650 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Cancer Research, 10 papers in Molecular Biology and 8 papers in Oncology. Recurrent topics in Subramanian Venkatesan's work include Cancer Genomics and Diagnostics (10 papers), Glioma Diagnosis and Treatment (4 papers) and Pancreatic and Hepatic Oncology Research (4 papers). Subramanian Venkatesan is often cited by papers focused on Cancer Genomics and Diagnostics (10 papers), Glioma Diagnosis and Treatment (4 papers) and Pancreatic and Hepatic Oncology Research (4 papers). Subramanian Venkatesan collaborates with scholars based in United Kingdom, Netherlands and United States. Subramanian Venkatesan's co-authors include Charles Swanton, Nicholas McGranahan, Jiří Bártek, Nnennaya Kanu, Sieger Leenstra, Reuben S. Harris, Martine L.M. Lamfers, Sandeep Singh, Gajendra P. S. Raghava and J Costello and has published in prestigious journals such as Nature Communications, PLoS ONE and The Journal of Comparative Neurology.

In The Last Decade

Subramanian Venkatesan

21 papers receiving 645 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Subramanian Venkatesan United Kingdom	12	404	225	171	99	71	22	650
Sebastian Großmann United Kingdom	6	400 1.0×	349 1.6×	274 1.6×	61 0.6×	79 1.1×	7	835
Wadie D. Mahauad‐Fernandez United States	11	583 1.4×	191 0.8×	248 1.5×	81 0.8×	186 2.6×	19	931
Stephen G. Gaffney United States	11	317 0.8×	266 1.2×	149 0.9×	108 1.1×	32 0.5×	25	566
Manasa Ramakrishna United Kingdom	15	894 2.2×	403 1.8×	275 1.6×	79 0.8×	103 1.5×	22	1.2k
Chiara Vardabasso United States	9	740 1.8×	120 0.5×	159 0.9×	56 0.6×	83 1.2×	12	873
Jamie K. Miller United States	14	568 1.4×	84 0.4×	423 2.5×	94 0.9×	74 1.0×	16	914
Daifeng Jiang United States	12	482 1.2×	279 1.2×	138 0.8×	44 0.4×	230 3.2×	22	794
Alexei L. Krasnoselsky United States	13	591 1.5×	159 0.7×	153 0.9×	64 0.6×	168 2.4×	15	1.0k
Carrie Bonomi United States	9	299 0.7×	242 1.1×	160 0.9×	55 0.6×	43 0.6×	10	518
Stéphanie Salesse France	16	326 0.8×	118 0.5×	121 0.7×	38 0.4×	88 1.2×	25	695

Countries citing papers authored by Subramanian Venkatesan

Since Specialization

Citations

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

Fields of papers citing papers by Subramanian Venkatesan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Subramanian Venkatesan

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

All Works

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20 of 20 papers shown

1.

Godin, Stephen K., Joshua Yang, Nisha Patel, et al.. (2025). BRCA2 deficiency and replication stress drive APOBEC3-Mediated genomic instability. Nature Communications. 16(1). 9544–9544.

2.

Venkatesan, Subramanian, et al.. (2025). Application of adaptive spotted hyena algorithm with deep efficient network for detecting parkinson’s disease. International Journal of Information Technology. 17(7). 4165–4180. 1 indexed citations

3.

Venkatesan, Subramanian, Dionysis Papadatos-Pastos, Tanya Ahmad, et al.. (2024). Entrectinib-Induced Myocarditis and Acute Heart Failure Responding to Steroid Treatment: A Case Report. JTO Clinical and Research Reports. 5(12). 100746–100746. 4 indexed citations

4.

Spoor, Jochem K. H., Clemens M.F. Dirven, Adam Pennycuick, et al.. (2024). Investigating chromosomal instability in long-term survivors with glioblastoma and grade 4 astrocytoma. Frontiers in Oncology. 13. 1218297–1218297. 4 indexed citations

5.

Venkatesan, Subramanian, Mihaela Angelova, Jiřina Bártková, et al.. (2022). APOBEC3 as a driver of genetic intratumor heterogeneity. Molecular & Cellular Oncology. 10(1). 2014734–2014734. 1 indexed citations

6.

Venkatesan, Subramanian, Rachel Rosenthal, Nnennaya Kanu, et al.. (2018). APOBEC mutagenesis in drug resistance and immune escape in HIV and cancer evolution. UCL Discovery (University College London). 1 indexed citations

7.

Venkatesan, Subramanian, Rachel Rosenthal, Nnennaya Kanu, et al.. (2018). Perspective: APOBEC mutagenesis in drug resistance and immune escape in HIV and cancer evolution. Annals of Oncology. 29(3). 563–572. 104 indexed citations

8.

Jaarsma, Dick, Bin Wu, Subramanian Venkatesan, et al.. (2018). The basal interstitial nucleus (BIN) of the cerebellum provides diffuse ascending inhibitory input to the floccular granule cell layer. The Journal of Comparative Neurology. 526(14). 2231–2256. 12 indexed citations

9.

Venkatesan, Subramanian, Charles Swanton, Barry S. Taylor, & J Costello. (2017). Treatment-Induced Mutagenesis and Selective Pressures Sculpt Cancer Evolution. Cold Spring Harbor Perspectives in Medicine. 7(8). a026617–a026617. 64 indexed citations

10.

Abbosh, Christopher, Subramanian Venkatesan, Sam M. Janes, Rebecca C. Fitzgerald, & Charles Swanton. (2017). Evolutionary dynamics in pre-invasive neoplasia. Current Opinion in Systems Biology. 2. 1–8. 5 indexed citations

11.

Venkatesan, Subramanian, Martine L.M. Lamfers, Sieger Leenstra, & Arnold G. Vulto. (2017). Overview of the patent expiry of (non-)tyrosine kinase inhibitors approved for clinical use in the EU and the US. Generics and Biosimilars Initiative Journal. 6(2). 89–96. 5 indexed citations

12.

Kanu, Nnennaya, Maria Antonietta Cerone, Gerald Goh, et al.. (2016). DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer. Genome biology. 17(1). 185–185. 118 indexed citations

13.

Venkatesan, Subramanian, et al.. (2016). Genetic biomarkers of drug response for small-molecule therapeutics targeting the RTK/Ras/PI3K, p53 or Rb pathway in glioblastoma. CNS Oncology. 5(2). 77–90. 36 indexed citations

14.

Patra, Sanjukta, et al.. (2016). MMpI: A WideRange of Available Compounds of Matrix Metalloproteinase Inhibitors. PLoS ONE. 11(8). e0159321–e0159321. 5 indexed citations

15.

Venkatesan, Subramanian & Charles Swanton. (2016). Tumor Evolutionary Principles: How Intratumor Heterogeneity Influences Cancer Treatment and Outcome. American Society of Clinical Oncology Educational Book. 36. e141–e149. 57 indexed citations

16.

Venkatesan, Subramanian, Marlous Hoogstraat, Jochem K. H. Spoor, et al.. (2016). TP53 mutated glioblastoma stem-like cell cultures are sensitive to dual mTORC1/2 inhibition while resistance in TP53 wild type cultures can be overcome by combined inhibition of mTORC1/2 and Bcl-2. Oncotarget. 7(36). 58435–58444. 9 indexed citations

17.

Venkatesan, Subramanian & Charles Swanton. (2016). Tumor Evolutionary Principles: How Intratumor Heterogeneity Influences Cancer Treatment and Outcome. American Society of Clinical Oncology Educational Book. e141–e149. 53 indexed citations

18.

Singh, Sandeep, et al.. (2015). AntiAngioPred: A Server for Prediction of Anti-Angiogenic Peptides. PLoS ONE. 10(9). e0136990–e0136990. 68 indexed citations

19.

Pont, Lotte M.E. Berghauser, Kishan A.T. Naipal, Jenneke J. Kloezeman, et al.. (2014). DNA damage response and anti-apoptotic proteins predict radiosensitization efficacy of HDAC inhibitors SAHA and LBH589 in patient-derived glioblastoma cells. Cancer Letters. 356(2). 525–535. 39 indexed citations

20.

Edwardraja, Selvakumar, et al.. (2010). Redesigning of anti‐c‐Met single chain Fv antibody for the cytoplasmic folding and its structural analysis. Biotechnology and Bioengineering. 106(3). 367–375. 14 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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