Heiko Enderling

5.3k total citations
118 papers, 3.2k citations indexed

About

Heiko Enderling is a scholar working on Oncology, Modeling and Simulation and Cancer Research. According to data from OpenAlex, Heiko Enderling has authored 118 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Oncology, 61 papers in Modeling and Simulation and 35 papers in Cancer Research. Recurrent topics in Heiko Enderling's work include Mathematical Biology Tumor Growth (61 papers), Cancer Cells and Metastasis (37 papers) and Cancer Genomics and Diagnostics (25 papers). Heiko Enderling is often cited by papers focused on Mathematical Biology Tumor Growth (61 papers), Cancer Cells and Metastasis (37 papers) and Cancer Genomics and Diagnostics (25 papers). Heiko Enderling collaborates with scholars based in United States, Germany and Poland. Heiko Enderling's co-authors include Philip Hahnfeldt, Lynn Hlatky, Mark A. J. Chaplain, Jan Poleszczuk, Alexander R.A. Anderson, Eduardo G. Moros, Renee Brady‐Nicholls, Jimmy J. Caudell, Louis B. Harrison and Jayant S. Vaidya and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Heiko Enderling

116 papers receiving 3.2k citations

Peers

Heiko Enderling
Philip Hahnfeldt United States
Russell C. Rockne United States
Jacob G. Scott United States
Laurence T. Baxter United States
Griffith R. Harsh United States
John T. Leith United States
John M.L. Ebos United States
Andriy Marusyk United States
Ivana Božić United States
Jason K. Rockhill United States
Philip Hahnfeldt United States
Heiko Enderling
Citations per year, relative to Heiko Enderling Heiko Enderling (= 1×) peers Philip Hahnfeldt

Countries citing papers authored by Heiko Enderling

Since Specialization
Citations

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

Fields of papers citing papers by Heiko Enderling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Heiko Enderling

This figure shows the co-authorship network connecting the top 25 collaborators of Heiko Enderling. A scholar is included among the top collaborators of Heiko Enderling 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 Heiko Enderling. Heiko Enderling 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.
Pasetto, S., Mohammad U. Zahid, Marilin Rosa, et al.. (2024). Calibrating tumor growth and invasion parameters with spectral spatial analysis of cancer biopsy tissues. npj Systems Biology and Applications. 10(1). 112–112. 1 indexed citations
2.
Zahid, Mohammad U., Shubhankar Nath, Tayyaba Hasan, et al.. (2024). Fractionated photoimmunotherapy stimulates an anti-tumour immune response: an integrated mathematical and in vitro study. British Journal of Cancer. 131(8). 1378–1386.
3.
4.
Browning, Alexander P., Ruth E. Baker, Philip K. Maini, et al.. (2024). Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling. Bulletin of Mathematical Biology. 86(2). 19–19. 8 indexed citations
5.
Enderling, Heiko, et al.. (2023). Simulating tumor volume dynamics in response to radiotherapy: Implications of model selection. Journal of Theoretical Biology. 576. 111656–111656. 2 indexed citations
6.
Zahid, Mohammad U., et al.. (2023). Proliferation Saturation Index to Simulate Adaptive Radiation Fractionation in HPV-Associated Oropharyngeal Cancer. International Journal of Radiation Oncology*Biology*Physics. 117(2). e495–e496. 2 indexed citations
7.
Drapaca, Corina, et al.. (2023). Modelling Radiation Cancer Treatment with a Death-Rate Term in Ordinary and Fractional Differential Equations. Bulletin of Mathematical Biology. 85(6). 47–47. 9 indexed citations
8.
Zahid, Mohammad U., Jennifer M. Binning, Bryan Q. Spring, et al.. (2022). Rethinking the immunotherapy numbers game. Journal for ImmunoTherapy of Cancer. 10(7). e005107–e005107. 12 indexed citations
9.
Brady‐Nicholls, Renee, Jingsong Zhang, Tian Zhang, et al.. (2021). Predicting patient-specific response to adaptive therapy in metastatic castration-resistant prostate cancer using prostate-specific antigen dynamics. Neoplasia. 23(9). 851–858. 29 indexed citations
10.
Spring, Bryan Q., Imran Rizvi, Robert M. Wenham, et al.. (2019). Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies. Frontiers in Physics. 7. 1 indexed citations
11.
Brady‐Nicholls, Renee & Heiko Enderling. (2019). Mathematical Models of Cancer: When to Predict Novel Therapies, and When Not to. Bulletin of Mathematical Biology. 81(10). 3722–3731. 111 indexed citations
12.
Poleszczuk, Jan, Shari Pilon‐Thomas, Sungjune Kim, et al.. (2018). Immune interconnectivity of anatomically distant tumors as a potential mediator of systemic responses to local therapy. Scientific Reports. 8(1). 9474–9474. 33 indexed citations
13.
Schoenfeld, Jonathan D., et al.. (2017). Evaluating the potential for maximized T cell redistribution entropy to improve abscopal responses to radiotherapy. PubMed. 3(3). 34001–34001. 6 indexed citations
14.
Poleszczuk, Jan, Kimberly A. Luddy, Mark Robertson‐Tessi, et al.. (2016). Abscopal Benefits of Localized Radiotherapy Depend on Activated T-cell Trafficking and Distribution between Metastatic Lesions. Cancer Research. 76(5). 1009–1018. 92 indexed citations
15.
Poleszczuk, Jan, Philip Hahnfeldt, & Heiko Enderling. (2015). Evolution and Phenotypic Selection of Cancer Stem Cells. PLoS Computational Biology. 11(3). e1004025–e1004025. 53 indexed citations
16.
Enderling, Heiko, Lynn Hlatky, & Philip Hahnfeldt. (2013). Cancer Stem Cells: A Minor Cancer Subpopulation that Redefines Global Cancer Features. Frontiers in Oncology. 3. 76–76. 51 indexed citations
17.
Gao, Xuefeng, Jackie McDonald, Lynn Hlatky, & Heiko Enderling. (2012). Acute and Fractionated Irradiation Differentially Modulate Glioma Stem Cell Division Kinetics. Cancer Research. 73(5). 1481–1490. 106 indexed citations
18.
Xu, Yan, Heiko Enderling, Daniel Park, et al.. (2011). Breaking the ‘harmony’ of TNF-α signaling for cancer treatment. Oncogene. 31(37). 4117–4127. 55 indexed citations
19.
Enderling, Heiko, Alexander R.A. Anderson, Mark A. J. Chaplain, et al.. (2009). Paradoxical Dependencies of Tumor Dormancy and Progression on Basic Cell Kinetics. Cancer Research. 69(22). 8814–8821. 140 indexed citations
20.
Enderling, Heiko, Nelson R. Alexander, Emily S. Clark, et al.. (2008). Dependence of Invadopodia Function on Collagen Fiber Spacing and Cross-Linking: Computational Modeling and Experimental Evidence. Biophysical Journal. 95(5). 2203–2218. 55 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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