Scott J. Bornheimer

1.7k total citations
17 papers, 655 citations indexed

About

Scott J. Bornheimer is a scholar working on Molecular Biology, Oncology and Infectious Diseases. According to data from OpenAlex, Scott J. Bornheimer has authored 17 papers receiving a total of 655 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 7 papers in Oncology and 5 papers in Infectious Diseases. Recurrent topics in Scott J. Bornheimer's work include SARS-CoV-2 and COVID-19 Research (4 papers), CAR-T cell therapy research (4 papers) and Gene Regulatory Network Analysis (3 papers). Scott J. Bornheimer is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (4 papers), CAR-T cell therapy research (4 papers) and Gene Regulatory Network Analysis (3 papers). Scott J. Bornheimer collaborates with scholars based in United States, Australia and Netherlands. Scott J. Bornheimer's co-authors include Marilyn G. Farquhar, Mikel Garcia‐Marcos, Pradipta Ghosh, Hirotaka Fukasawa, Krystyna Kudlicka, Wei Huang, Suraj Saksena, Julie Bérubé, Mano R. Maurya and Maurice R.G. O’Gorman and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Cell Biology and Blood.

In The Last Decade

Scott J. Bornheimer

14 papers receiving 650 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Scott J. Bornheimer United States 9 359 163 86 84 77 17 655
Jin‐Ping Li Sweden 10 313 0.9× 115 0.7× 50 0.6× 15 0.2× 82 1.1× 13 627
Sagi Tshori Israel 18 256 0.7× 27 0.2× 115 1.3× 25 0.3× 170 2.2× 40 698
Gloria Riitano Italy 17 230 0.6× 44 0.3× 23 0.3× 32 0.4× 19 0.2× 34 548
Pascale Galéa France 13 234 0.7× 44 0.3× 107 1.2× 14 0.2× 57 0.7× 28 633
Peter Ulrichts Belgium 13 192 0.5× 101 0.6× 241 2.8× 11 0.1× 75 1.0× 33 906
Xaria X. Li Australia 13 244 0.7× 45 0.3× 24 0.3× 40 0.5× 54 0.7× 29 673
Lavinia M. Proctor Australia 11 190 0.5× 23 0.1× 62 0.7× 36 0.4× 42 0.5× 12 636
Francisco Ramírez‐Valle United States 12 275 0.8× 30 0.2× 17 0.2× 13 0.2× 67 0.9× 22 816
Jeanette Schwarz Germany 14 255 0.7× 63 0.4× 18 0.2× 5 0.1× 203 2.6× 17 586
Maria Stensland Norway 12 178 0.5× 29 0.2× 21 0.2× 13 0.2× 41 0.5× 30 438

Countries citing papers authored by Scott J. Bornheimer

Since Specialization
Citations

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

Fields of papers citing papers by Scott J. Bornheimer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Scott J. Bornheimer. 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 Scott J. Bornheimer. The network helps show where Scott J. Bornheimer may publish in the future.

Co-authorship network of co-authors of Scott J. Bornheimer

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

All Works

17 of 17 papers shown
1.
Foreman, Taylor W., et al.. (2025). Automation of flow cytometry data analysis with elastic image registration. Scientific Reports. 15(1). 16949–16949.
2.
Hartley, Gemma E., Paul A. Gill, Irene Boo, et al.. (2024). Homologous but not heterologous COVID-19 vaccine booster elicits IgG4+ B-cells and enhanced Omicron subvariant binding. npj Vaccines. 9(1). 129–129. 8 indexed citations
3.
Giuliano, A. E., Angela Chen, Chih‐Huang Lai, et al.. (2024). Automated analysis of flow cytometry data with minimal training files: Research evaluation of an elastic image registration algorithm for TBNK, stem cell enumeration, and lymphoid screening tube assays. Cytometry Part B Clinical Cytometry. 108(5). 357–365. 2 indexed citations
4.
Spiegel, Jay Y., Jean Oak, Anmol Goyal, et al.. (2024). CD19 Antigen Density Down-Regulation at Time of Progression in Large B-Cell Lymphoma Patients Treated with Axicabtagene Ciloleucel. Transplantation and Cellular Therapy. 30(2). S204–S204.
5.
Hartley, Gemma E., Emily S.J. Edwards, Nirupama Varese, et al.. (2023). COVID-19 Adenoviral Vector Vaccination Elicits a Robust Memory B Cell Response with the Capacity to Recognize Omicron BA.2 and BA.5 Variants. Journal of Clinical Immunology. 43(7). 1506–1518. 10 indexed citations
6.
7.
Radosevich, Molly, Scott J. Bornheimer, Bita Sahaf, et al.. (2023). Antigen density quantification of cell-surface immunotherapy targets by flow cytometry: Multi-antigen assay of neuroblastoma bone marrow metastasis. STAR Protocols. 4(4). 102709–102709.
8.
Huang, Wei, Julie Bérubé, Michelle McNamara, et al.. (2020). Lymphocyte Subset Counts in COVID‐19 Patients: A Meta‐Analysis. Cytometry Part A. 97(8). 772–776. 160 indexed citations
9.
Scarfò, Irene, Kathleen Gallagher, Marcela V. Maus, et al.. (2019). Application of a Standardized Flow Cytometry Panel for Defining and Monitoring the Immunophenotype of CAR-T Cells. Blood. 134(Supplement_1). 5626–5626. 1 indexed citations
10.
Bornheimer, Scott J., et al.. (2016). Clinical Evaluation of the BD FACSPresto™ Near-Patient CD4 Counter in Kenya. PLoS ONE. 11(8). e0157939–e0157939. 14 indexed citations
11.
Ghosh, Pradipta, Scott J. Bornheimer, Mikel Garcia‐Marcos, et al.. (2010). A Gαi–GIV Molecular Complex Binds Epidermal Growth Factor Receptor and Determines Whether Cells Migrate or Proliferate. Molecular Biology of the Cell. 21(13). 2338–2354. 124 indexed citations
12.
Fukasawa, Hirotaka, Scott J. Bornheimer, Krystyna Kudlicka, & Marilyn G. Farquhar. (2009). Slit Diaphragms Contain Tight Junction Proteins. Journal of the American Society of Nephrology. 20(7). 1491–1503. 120 indexed citations
13.
Maurya, Mano R., Scott J. Bornheimer, Venkat Venkatasubramanian, & Shankar Subramaniam. (2009). Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks. IET Systems Biology. 3(1). 24–39. 12 indexed citations
14.
Ghosh, Pradipta, Mikel Garcia‐Marcos, Scott J. Bornheimer, & Marilyn G. Farquhar. (2008). Activation of Gαi3 triggers cell migration via regulation of GIV. The Journal of Cell Biology. 182(2). 381–393. 127 indexed citations
15.
Garcia‐Marcos, Mikel, Pradipta Ghosh, Scott J. Bornheimer, & Marilyn G. Farquhar. (2008). Activation of a Gαi3‐GIV‐Molecular‐Switch Triggers Cell Migration. The FASEB Journal. 22(S2). 283–283. 2 indexed citations
16.
Maurya, Mano R., Scott J. Bornheimer, Venkat Venkatasubramanian, & Shankar Subramaniam. (2005). Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module. PubMed. 152(4). 229–229. 34 indexed citations
17.
Bornheimer, Scott J., Mano R. Maurya, Marilyn G. Farquhar, & Shankar Subramaniam. (2004). Computational modeling reveals how interplay between components of a GTPase-cycle module regulates signal transduction. Proceedings of the National Academy of Sciences. 101(45). 15899–15904. 33 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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