Jeff Hammerbacher

3.8k total citations
20 papers, 1.1k citations indexed

About

Jeff Hammerbacher is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Jeff Hammerbacher has authored 20 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 10 papers in Immunology and 7 papers in Oncology. Recurrent topics in Jeff Hammerbacher's work include Immunotherapy and Immune Responses (7 papers), CAR-T cell therapy research (5 papers) and Cancer Immunotherapy and Biomarkers (4 papers). Jeff Hammerbacher is often cited by papers focused on Immunotherapy and Immune Responses (7 papers), CAR-T cell therapy research (5 papers) and Cancer Immunotherapy and Biomarkers (4 papers). Jeff Hammerbacher collaborates with scholars based in United States, Australia and United Kingdom. Jeff Hammerbacher's co-authors include Alex Rubinsteyn, Bülent Arman Aksoy, Uri Laserson, Timothy J. O’Donnell, Maria Bonsack, Angelika B. Riemer, Alexandra Snyder, Arun Ahuja, Matthew E. Berginski and Erika Deoudes and has published in prestigious journals such as Journal of Clinical Investigation, Journal of Clinical Oncology and Blood.

In The Last Decade

Jeff Hammerbacher

19 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jeff Hammerbacher United States 15 538 467 455 148 97 20 1.1k
Bingliang Chen China 17 524 1.0× 281 0.6× 385 0.8× 103 0.7× 95 1.0× 47 1.1k
Lars Rønn Olsen Denmark 22 676 1.3× 397 0.9× 282 0.6× 107 0.7× 167 1.7× 51 1.3k
Weiwen Yang China 19 492 0.9× 581 1.2× 697 1.5× 84 0.6× 126 1.3× 48 1.4k
Chichung Wang United States 9 446 0.8× 604 1.3× 419 0.9× 74 0.5× 96 1.0× 15 1.3k
Georgios Georgakis United States 21 830 1.5× 368 0.8× 511 1.1× 73 0.5× 166 1.7× 55 1.6k
Jeffrey Chun Tatt Lim Singapore 22 505 0.9× 413 0.9× 680 1.5× 89 0.6× 326 3.4× 52 1.5k
Farah Khalil United States 15 315 0.6× 452 1.0× 663 1.5× 143 1.0× 144 1.5× 37 1.3k
Salil S. Bhate United States 9 1.2k 2.3× 440 0.9× 439 1.0× 79 0.5× 235 2.4× 16 1.8k
Arianna Palladini Italy 18 439 0.8× 287 0.6× 459 1.0× 169 1.1× 131 1.4× 48 964
Asha Das United States 24 511 0.9× 324 0.7× 395 0.9× 138 0.9× 204 2.1× 54 1.6k

Countries citing papers authored by Jeff Hammerbacher

Since Specialization
Citations

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

Fields of papers citing papers by Jeff Hammerbacher

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jeff Hammerbacher

This figure shows the co-authorship network connecting the top 25 collaborators of Jeff Hammerbacher. A scholar is included among the top collaborators of Jeff Hammerbacher 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 Jeff Hammerbacher. Jeff Hammerbacher 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.
Jeffery, Ben, Timothy R. Millar, Benjamin Elsworth, et al.. (2025). Analysis-ready VCF at Biobank scale using Zarr. GigaScience. 14. 1 indexed citations
2.
Yellen, Benjamin B., Jon S. Zawistowski, Elliott D. SoRelle, et al.. (2021). Massively parallel quantification of phenotypic heterogeneity in single-cell drug responses. Science Advances. 7(38). eabf9840–eabf9840. 14 indexed citations
3.
Sanseviero, Emilio, Erin M. O’Brien, Tamer B. Shabaneh, et al.. (2019). Anti–CTLA-4 Activates Intratumoral NK Cells and Combined with IL15/IL15Rα Complexes Enhances Tumor Control. Cancer Immunology Research. 7(8). 1371–1380. 51 indexed citations
4.
Aksoy, Bülent Arman, et al.. (2019). Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging. BMC Bioinformatics. 20(1). 448–448. 34 indexed citations
5.
Yamamoto, Tori N., Ping‐Hsien Lee, Suman K. Vodnala, et al.. (2019). T cells genetically engineered to overcome death signaling enhance adoptive cancer immunotherapy. Journal of Clinical Investigation. 129(4). 1551–1565. 110 indexed citations
6.
Blázquez, Ana-Belén, Alex Rubinsteyn, Julia Kodysh, et al.. (2019). A phase I study of the safety and immunogenicity of a multi-peptide personalized genomic vaccine in the adjuvant treatment of solid tumors and hematological malignancies.. Journal of Clinical Oncology. 37(15_suppl). e14307–e14307. 4 indexed citations
7.
O’Donnell, Timothy J., Alex Rubinsteyn, Maria Bonsack, et al.. (2018). MHCflurry: Open-Source Class I MHC Binding Affinity Prediction. Cell Systems. 7(1). 129–132.e4. 273 indexed citations
8.
O’Donnell, Timothy J., Elizabeth L. Christie, Arun Ahuja, et al.. (2018). Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer. BMC Cancer. 18(1). 87–87. 29 indexed citations
9.
Deoudes, Erika, Matthew E. Berginski, Bülent Arman Aksoy, et al.. (2018). Coral: Clear and Customizable Visualization of Human Kinome Data. Cell Systems. 7(3). 347–350.e1. 111 indexed citations
10.
Olcina, Monica M., Nikolas G. Balanis, Ryan Kim, et al.. (2018). Mutations in an Innate Immunity Pathway Are Associated with Poor Overall Survival Outcomes and Hypoxic Signaling in Cancer. Cell Reports. 25(13). 3721–3732.e6. 26 indexed citations
11.
Rubinsteyn, Alex, Julia Kodysh, Bülent Arman Aksoy, et al.. (2018). Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial. Frontiers in Immunology. 8. 1807–1807. 49 indexed citations
12.
Bowers, Jacob S., Kinga Majchrzak, Michelle H. Nelson, et al.. (2017). PI3Kδ Inhibition Enhances the Antitumor Fitness of Adoptively Transferred CD8+ T Cells. Frontiers in Immunology. 8. 1221–1221. 61 indexed citations
13.
Hammerbacher, Jeff & Alexandra Snyder. (2017). Informatics for cancer immunotherapy. Annals of Oncology. 28(suppl_12). xii56–xii73. 18 indexed citations
14.
Nathanson, Tavi, Arun Ahuja, Alex Rubinsteyn, et al.. (2016). Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade. Cancer Immunology Research. 5(1). 84–91. 122 indexed citations
15.
Aksoy, Bülent Arman, et al.. (2016). pileup.js: a JavaScript library for interactive and in-browser visualization of genomic data. Bioinformatics. 32(15). 2378–2379. 15 indexed citations
16.
Oermann, Eric K., Alex Rubinsteyn, Dale Ding, et al.. (2016). Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations. Scientific Reports. 6(1). 21161–21161. 83 indexed citations
17.
Nothaft, Frank Austin, Timothy Danford, Zhao Zhang, et al.. (2015). Rethinking Data-Intensive Science Using Scalable Analytics Systems. 631–646. 64 indexed citations
18.
Perumal, Deepak, Antonio Simone Laganà, Alex Rubinsteyn, et al.. (2015). Patient-Specific Mutation-Derived Tumor Antigens As Targets for Cancer Immunotherapy in Multiple Myeloma. Blood. 126(23). 1851–1851.
19.
Finnigan, John P., Alex Rubinsteyn, Jeff Hammerbacher, & Nina Bhardwaj. (2015). Mutation-Derived Tumor Antigens: Novel Targets in Cancer Immunotherapy.. PubMed. 29(12). 970–2, 974. 8 indexed citations
20.
Demiralp, Çağatay, Eric J. Hayden, Jeff Hammerbacher, & Jeffrey Heer. (2013). invis: Exploring high-dimensional RNA sequences from in vitro selection. 27. 1–8. 7 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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