Satwik Rajaram

1.2k total citations
23 papers, 668 citations indexed

About

Satwik Rajaram is a scholar working on Cancer Research, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Satwik Rajaram has authored 23 papers receiving a total of 668 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Cancer Research, 9 papers in Molecular Biology and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Satwik Rajaram's work include Cancer Genomics and Diagnostics (10 papers), Renal cell carcinoma treatment (8 papers) and Cell Image Analysis Techniques (6 papers). Satwik Rajaram is often cited by papers focused on Cancer Genomics and Diagnostics (10 papers), Renal cell carcinoma treatment (8 papers) and Cell Image Analysis Techniques (6 papers). Satwik Rajaram collaborates with scholars based in United States, France and Argentina. Satwik Rajaram's co-authors include Y. Oono, Lani F. Wu, Steven J. Altschuler, Robert J. Steininger, Anwu Zhou, Kevin Thurley, Louise Evans, Prasad Koduru, Maike Roth and Bruce A. Posner and has published in prestigious journals such as Nature Communications, Bioinformatics and Cancer Cell.

In The Last Decade

Satwik Rajaram

22 papers receiving 659 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Satwik Rajaram United States 11 399 217 201 194 43 23 668
Matthew Ung United States 13 521 1.3× 237 1.1× 77 0.4× 148 0.8× 25 0.6× 33 786
Bhairavi Tolani United States 14 390 1.0× 123 0.6× 117 0.6× 226 1.2× 20 0.5× 18 743
Yiyu Dong United States 16 731 1.8× 203 0.9× 220 1.1× 328 1.7× 19 0.4× 26 1.2k
Simona Cristea United States 14 476 1.2× 303 1.4× 90 0.4× 214 1.1× 21 0.5× 21 699
Robert Cornelison United States 18 770 1.9× 230 1.1× 115 0.6× 297 1.5× 41 1.0× 29 1.1k
Gerald Fontenay United States 10 288 0.7× 124 0.6× 55 0.3× 143 0.7× 56 1.3× 17 479
Ronald Realubit United States 11 441 1.1× 154 0.7× 156 0.8× 109 0.6× 15 0.3× 18 734
Deena M.A. Gendoo Canada 15 528 1.3× 322 1.5× 117 0.6× 336 1.7× 19 0.4× 25 990
Motohiro Yamauchi Japan 14 628 1.6× 136 0.6× 233 1.2× 216 1.1× 14 0.3× 44 895
Angelo Gámez‐Pozo Spain 15 427 1.1× 257 1.2× 151 0.8× 169 0.9× 8 0.2× 51 718

Countries citing papers authored by Satwik Rajaram

Since Specialization
Citations

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

Fields of papers citing papers by Satwik Rajaram

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Satwik Rajaram

This figure shows the co-authorship network connecting the top 25 collaborators of Satwik Rajaram. A scholar is included among the top collaborators of Satwik Rajaram 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 Satwik Rajaram. Satwik Rajaram 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.
Rakheja, Dinesh, et al.. (2025). Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial. Nature Communications. 16(1). 2610–2610. 11 indexed citations
2.
Nielsen, Anne L., Mehrdad Nourani, Dinesh Rakheja, et al.. (2025). MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology. Genome Medicine. 17(1). 101–101.
3.
Khene, Z., Isamu Tachibana, Raj Bhanvadia, et al.. (2024). Clinical application of radiomics for the prediction of treatment outcome and survival in patients with renal cell carcinoma: a systematic review. World Journal of Urology. 42(1). 541–541. 3 indexed citations
4.
Christie, Alana, Vitaly Margulis, Dinesh Rakheja, et al.. (2022). Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning. Cancer Research. 82(15). 2792–2806. 21 indexed citations
5.
Kapur, Payal, Satwik Rajaram, & James Brugarolas. (2022). The expanding role of BAP1 in clear cell renal cell carcinoma. Human Pathology. 133. 22–31. 16 indexed citations
6.
Vega, Anthony R., Ping Shang, Chan Foong, et al.. (2021). Deep learning reveals disease-specific signatures of white matter pathology in tauopathies. Acta Neuropathologica Communications. 9(1). 170–170. 12 indexed citations
7.
Kapur, Payal, Alana Christie, Satwik Rajaram, & James Brugarolas. (2020). What morphology can teach us about renal cell carcinoma clonal evolution. PubMed. 18(3). 68–76. 16 indexed citations
8.
Brugarolas, James, Satwik Rajaram, Alana Christie, & Payal Kapur. (2020). The Evolution of Angiogenic and Inflamed Tumors: The Renal Cancer Paradigm. Cancer Cell. 38(6). 771–773. 24 indexed citations
9.
Cai, Qi, Alana Christie, Satwik Rajaram, et al.. (2019). Ontological analyses reveal clinically-significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type. EBioMedicine. 51. 102526–102526. 35 indexed citations
10.
Rajaram, Satwik, Maike Roth, Scott R. VandenBerg, et al.. (2019). A multi-modal data resource for investigating topographic heterogeneity in patient-derived xenograft tumors. Scientific Data. 6(1). 253–253. 6 indexed citations
11.
Cimino, Patrick J., Kimmo J. Hatanpaa, Charles L. White, et al.. (2019). Distinct Expression Patterns of Carbonic Anhydrase IX in Clear Cell, Microcystic, and Angiomatous Meningiomas. Journal of Neuropathology & Experimental Neurology. 78(12). 1081–1088. 6 indexed citations
12.
Deb, Dhruba, Satwik Rajaram, Jill E. Larsen, et al.. (2017). Combination Therapy Targeting BCL6 and Phospho-STAT3 Defeats Intratumor Heterogeneity in a Subset of Non–Small Cell Lung Cancers. Cancer Research. 77(11). 3070–3081. 33 indexed citations
13.
Rajaram, Satwik, John D. Gordan, Agnieszka K. Witkiewicz, et al.. (2017). Sampling strategies to capture single-cell heterogeneity. Nature Methods. 14(10). 967–970. 24 indexed citations
14.
Rajaram, Satwik, Agnieszka K. Witkiewicz, James S. Malter, et al.. (2017). Sampling Strategies To Capture Single-Cell Heterogeneity. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
15.
Ramirez, Michael E., Satwik Rajaram, Robert J. Steininger, et al.. (2016). Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. Nature Communications. 7(1). 10690–10690. 362 indexed citations
16.
Pavie, Benjamin, Satwik Rajaram, Jason M. Altschuler, et al.. (2014). Rapid Analysis and Exploration of Fluorescence Microscopy Images. Journal of Visualized Experiments. 1 indexed citations
17.
Steininger, Robert J., Satwik Rajaram, Luc Girard, et al.. (2014). On comparing heterogeneity across biomarkers. Cytometry Part A. 87(6). 558–567. 10 indexed citations
18.
Rajaram, Satwik & Y. Oono. (2010). NeatMap - non-clustering heat map alternatives in R. BMC Bioinformatics. 11(1). 45–45. 75 indexed citations
19.
Rajaram, Satwik. (2009). A novel meta-analysis method exploiting consistency of high-throughput experiments. Bioinformatics. 25(5). 636–642. 3 indexed citations
20.
Rajaram, Satwik, et al.. (1983). Numerical modelling of laser material processing. 216–223. 2 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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