Sharmila A. Bapat

3.2k total citations · 2 hit papers
53 papers, 2.5k citations indexed

About

Sharmila A. Bapat is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Sharmila A. Bapat has authored 53 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 20 papers in Oncology and 15 papers in Cancer Research. Recurrent topics in Sharmila A. Bapat's work include Cancer Cells and Metastasis (18 papers), Epigenetics and DNA Methylation (11 papers) and Cancer Genomics and Diagnostics (8 papers). Sharmila A. Bapat is often cited by papers focused on Cancer Cells and Metastasis (18 papers), Epigenetics and DNA Methylation (11 papers) and Cancer Genomics and Diagnostics (8 papers). Sharmila A. Bapat collaborates with scholars based in India, United States and United Kingdom. Sharmila A. Bapat's co-authors include Nawneet K. Kurrey, Avinash M. Mali, Chaitanyananda B. Koppikar, Anjali P. Kusumbe, Avinash Ghanate, Swati Jalgaonkar, Alok V. Joglekar, Prasad Chaskar, Rajkumar Singh Kalra and Mohit Kumar Jolly and has published in prestigious journals such as PLoS ONE, Cancer Research and Oncogene.

In The Last Decade

Sharmila A. Bapat

51 papers receiving 2.5k citations

Hit Papers

Stem and Progenitor-Like Cells Contribute to the Aggressi... 2005 2026 2012 2019 2005 2009 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sharmila A. Bapat India 20 1.6k 1.5k 741 236 232 53 2.5k
Paola Ostano Italy 25 1.2k 0.8× 703 0.5× 552 0.7× 68 0.3× 201 0.9× 59 2.1k
Juan Martín‐Caballero Spain 29 1.9k 1.2× 1.4k 0.9× 376 0.5× 50 0.2× 215 0.9× 43 3.0k
Eiji Toyoda Japan 18 1.2k 0.7× 788 0.5× 320 0.4× 94 0.4× 121 0.5× 31 1.8k
Chang‐Il Hwang United States 19 936 0.6× 559 0.4× 589 0.8× 142 0.6× 96 0.4× 34 1.6k
Leticia Oliveira‐Ferrer Germany 25 1.4k 0.8× 712 0.5× 645 0.9× 86 0.4× 201 0.9× 76 2.3k
Tsz-Lun Yeung United States 15 1.3k 0.8× 514 0.4× 875 1.2× 263 1.1× 129 0.6× 28 1.9k
Robin E. Bachelder United States 22 1.7k 1.1× 1.2k 0.8× 573 0.8× 41 0.2× 297 1.3× 37 2.6k
Ellen Wientjens Netherlands 17 2.4k 1.5× 1.8k 1.2× 420 0.6× 78 0.3× 190 0.8× 18 3.3k
Peter Bouwman Netherlands 22 2.5k 1.5× 1.1k 0.8× 400 0.5× 114 0.5× 319 1.4× 42 3.4k
Purificacı́on Muñoz Spain 29 2.5k 1.5× 1.1k 0.8× 755 1.0× 35 0.1× 189 0.8× 35 3.8k

Countries citing papers authored by Sharmila A. Bapat

Since Specialization
Citations

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

Fields of papers citing papers by Sharmila A. Bapat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sharmila A. Bapat. 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 Sharmila A. Bapat. The network helps show where Sharmila A. Bapat may publish in the future.

Co-authorship network of co-authors of Sharmila A. Bapat

This figure shows the co-authorship network connecting the top 25 collaborators of Sharmila A. Bapat. A scholar is included among the top collaborators of Sharmila A. Bapat 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 Sharmila A. Bapat. Sharmila A. Bapat 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.
Kulkarni, Swati, et al.. (2025). Chimerism: A whole new perspective in gene regulation. Biochimica et Biophysica Acta (BBA) - General Subjects. 1869(3). 130767–130767.
2.
Mali, Avinash M., et al.. (2024). A novel ITGB8 transcript variant sustains ovarian cancer cell survival through genomic instability and altered ploidy on a mutant p53 background. Journal of Ovarian Research. 17(1). 218–218. 1 indexed citations
4.
Singh, Divya, et al.. (2022). RBM47 is a Critical Regulator of Mouse Embryonic Stem Cell Differentiation. Stem Cell Reviews and Reports. 19(2). 475–490. 2 indexed citations
5.
Kalra, Rajkumar Singh, et al.. (2021). A monoclonal antibody against annexin A2 targets stem and progenitor cell fractions in tumors. Translational Oncology. 15(1). 101257–101257. 3 indexed citations
7.
Kamble, Swapnil C., et al.. (2019). Clinical Stratification of High-Grade Ovarian Serous Carcinoma Using a Panel of Six Biomarkers. Journal of Clinical Medicine. 8(3). 330–330. 5 indexed citations
8.
Singh, Anand K., et al.. (2015). Evaluation of Epigenetic Drug Targeting of Heterogenous Tumor Cell Fractions Using Potential Biomarkers of Response in Ovarian Cancer. Clinical Cancer Research. 21(22). 5151–5163. 15 indexed citations
9.
Kumar, Brijesh, et al.. (2015). Auto-regulation of Slug mediates its activity during epithelial to mesenchymal transition. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 1849(9). 1209–1218. 15 indexed citations
10.
Singh, Anand K., et al.. (2015). A tumor deconstruction platform identifies definitive end points in the evaluation of drug responses. Oncogene. 35(6). 727–737. 9 indexed citations
11.
Dong, Ying, et al.. (2014). Transforming the future of treatment for ovarian cancer. QUT ePrints (Queensland University of Technology). 1 indexed citations
12.
Gardi, Nilesh, et al.. (2013). Discrete Molecular Classes of Ovarian Cancer Suggestive of Unique Mechanisms of Transformation and Metastases. Clinical Cancer Research. 20(1). 87–99. 35 indexed citations
13.
Bapat, Sharmila A., Anagha Krishnan, Avinash Ghanate, Anjali P. Kusumbe, & Rajkumar Singh Kalra. (2010). Gene Expression: Protein Interaction Systems Network Modeling Identifies Transformation-Associated Molecules and Pathways in Ovarian Cancer. Cancer Research. 70(12). 4809–4819. 34 indexed citations
14.
Sharma, Neeti, et al.. (2010). CREBBP Re-arrangements affect protein function and lead to aberrant neuronal differentiation. Differentiation. 79(4-5). 218–231. 14 indexed citations
15.
Sharma, Neeti, Avinash M. Mali, & Sharmila A. Bapat. (2010). Spectrum of CREBBP mutations in Indian patients with Rubinstein-Taybi syndrome. Journal of Biosciences. 35(2). 187–202. 14 indexed citations
16.
Kusumbe, Anjali P. & Sharmila A. Bapat. (2009). Cancer Stem Cells and Aneuploid Populations within Developing Tumors Are the Major Determinants of Tumor Dormancy. Cancer Research. 69(24). 9245–9253. 142 indexed citations
17.
Bapat, Sharmila A.. (2009). Cancer stem cells : identification and targets. John Wiley & Sons eBooks. 3 indexed citations
18.
Wani, Aijaz A., Ashraf Yusuf Rangrez, Himanshu Kumar, et al.. (2008). Analysis of reactive oxygen species and antioxidant defenses in complex I deficient patients revealed a specific increase in superoxide dismutase activity. Free Radical Research. 42(5). 415–427. 7 indexed citations
19.
Wani, Aijaz A., Sharmila A. Bapat, Ashraf Yusuf Rangrez, et al.. (2007). Analysis of Mitochondrial DNA Sequences in Childhood Encephalomyopathies Reveals New Disease-Associated Variants. PLoS ONE. 2(9). e942–e942. 12 indexed citations
20.
Bapat, Sharmila A., Avinash M. Mali, Chaitanyananda B. Koppikar, & Nawneet K. Kurrey. (2005). Stem and Progenitor-Like Cells Contribute to the Aggressive Behavior of Human Epithelial Ovarian Cancer. Cancer Research. 65(8). 3025–3029. 608 indexed citations breakdown →

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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