Jasreet Hundal

12.6k total citations · 2 hit papers
21 papers, 3.1k citations indexed

About

Jasreet Hundal is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Jasreet Hundal has authored 21 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 13 papers in Immunology and 9 papers in Oncology. Recurrent topics in Jasreet Hundal's work include Immunotherapy and Immune Responses (13 papers), vaccines and immunoinformatics approaches (10 papers) and Monoclonal and Polyclonal Antibodies Research (7 papers). Jasreet Hundal is often cited by papers focused on Immunotherapy and Immune Responses (13 papers), vaccines and immunoinformatics approaches (10 papers) and Monoclonal and Polyclonal Antibodies Research (7 papers). Jasreet Hundal collaborates with scholars based in United States, Switzerland and Austria. Jasreet Hundal's co-authors include Elaine R. Mardis, Vincent Magrini, Beatriz M. Carreno, Gerald P. Linette, Allegra A. Petti, Saghar Kaabinejadian, Michelle Becker‐Hapak, William H. Hildebrand, Wen‐Rong Lie and Amy Ly and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Jasreet Hundal

20 papers receiving 3.1k citations

Hit Papers

A dendritic cell vaccine increases the breadth and divers... 2012 2026 2016 2021 2015 2012 250 500 750

Peers

Jasreet Hundal
Gundram Jung Germany
Mark R. Albertini United States
Pia Kvistborg Netherlands
Eric R. Lutz United States
Todd D. Armstrong United States
Anna Pasetto United States
Petra Baumgaertner Switzerland
Hung T. Khong United States
Arnob Banerjee United States
Gundram Jung Germany
Jasreet Hundal
Citations per year, relative to Jasreet Hundal Jasreet Hundal (= 1×) peers Gundram Jung

Countries citing papers authored by Jasreet Hundal

Since Specialization
Citations

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

Fields of papers citing papers by Jasreet Hundal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jasreet Hundal

This figure shows the co-authorship network connecting the top 25 collaborators of Jasreet Hundal. A scholar is included among the top collaborators of Jasreet Hundal 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 Jasreet Hundal. Jasreet Hundal 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.
Kiwala, Susanna, Zachary L. Skidmore, Jonathan J Song, et al.. (2024). pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection. Genome Medicine. 16(1). 132–132. 1 indexed citations
2.
Skidmore, Zachary L., Jason Kunisaki, Yiing Lin, et al.. (2022). Genomic and transcriptomic somatic alterations of hepatocellular carcinoma in non-cirrhotic livers. Cancer Genetics. 264-265. 90–99. 6 indexed citations
3.
Hundal, Jasreet, Susanna Kiwala, Joshua F. McMichael, et al.. (2020). pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens. Cancer Immunology Research. 8(3). 409–420. 145 indexed citations
4.
Mattos‐Arruda, Leticia De, Miguél Vázquez, Francesca Finotello, et al.. (2020). Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group. Annals of Oncology. 31(8). 978–990. 99 indexed citations
5.
Cullinan, Darren R., Michael D. McLellan, Tammi L. Vickery, et al.. (2020). Preliminary Results Of A Phase Ib Clinical Trial Of A Neoantigen Dna Vaccine For Pancreatic Cancer. HPB. 22. S12–S13. 3 indexed citations
6.
Hundal, Jasreet, Susanna Kiwala, Yang-Yang Feng, et al.. (2018). Accounting for proximal variants improves neoantigen prediction. Nature Genetics. 51(1). 175–179. 31 indexed citations
7.
Noguchi, Takuro, Jeffrey P. Ward, Matthew M. Gubin, et al.. (2017). Temporally Distinct PD-L1 Expression by Tumor and Host Cells Contributes to Immune Escape. Cancer Immunology Research. 5(2). 106–117. 242 indexed citations
8.
Lesurf, Robert, Obi L. Griffith, Malachi Griffith, et al.. (2017). Genomic characterization of HER2-positive breast cancer and response to neoadjuvant trastuzumab and chemotherapy—results from the ACOSOG Z1041 (Alliance) trial. Annals of Oncology. 28(5). 1070–1077. 47 indexed citations
9.
Hundal, Jasreet, Beatriz M. Carreno, Allegra A. Petti, et al.. (2016). pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Medicine. 8(1). 11–11. 267 indexed citations
10.
Hundal, Jasreet, Christopher A. Miller, Malachi Griffith, et al.. (2016). Cancer Immunogenomics: Computational Neoantigen Identification and Vaccine Design. Cold Spring Harbor Symposia on Quantitative Biology. 81. 105–111. 19 indexed citations
11.
Hundal, Jasreet, Beatriz M. Carreno, Allegra A. Petti, et al.. (2016). Abstract 3995: pVAC-Seq: A genome-guided in silico approach to identify tumor neoantigens for personalized immunotherapy. Cancer Research. 76(14_Supplement). 3995–3995. 1 indexed citations
12.
Carreno, Beatriz M., Vincent Magrini, Michelle Becker‐Hapak, et al.. (2015). A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science. 348(6236). 803–808. 997 indexed citations breakdown →
13.
Carreno, Beatriz M., Vincent Magrini, Michelle Becker‐Hapak, et al.. (2015). Abstract LB-237: Vaccination increases the breadth and diversity of melanoma neoantigen-specific T cells in humans. Cancer Research. 75(15_Supplement). LB–237. 1 indexed citations
14.
Okeyo-Owuor, Theresa, Brian S. White, Rakesh Chatrikhi, et al.. (2014). U2AF1 mutations alter sequence specificity of pre-mRNA binding and splicing. Leukemia. 29(4). 909–917. 97 indexed citations
15.
Celik, Hamza, Cates Mallaney, Christopher A. Miller, et al.. (2014). Enforced Differentiation of Dnmt3a-Null Bone Marrow Leads to Failure with c-Kit Mutations Driving Leukemic Transformation. Blood. 124(21). 837–837. 2 indexed citations
16.
Celik, Hamza, Cates Mallaney, Elizabeth L. Ostrander, et al.. (2014). Enforced differentiation of Dnmt3a-null bone marrow leads to failure with c-Kit mutations driving leukemic transformation. Blood. 125(4). 619–628. 77 indexed citations
17.
Trissal, Maria, Jessica Silva-Fisher, Todd Wylie, et al.. (2013). Dysregulation and Recurrent Mutation Of miRNA-142 In De Novo AML. Blood. 122(21). 472–472. 3 indexed citations
18.
Klco, Jeffery M., David H. Spencer, Tamara Lamprecht, et al.. (2013). Genomic impact of transient low-dose decitabine treatment on primary AML cells. Blood. 121(9). 1633–1643. 106 indexed citations
19.
Matsushita, Hirokazu, Matthew D. Vesely, Daniel C. Koboldt, et al.. (2012). Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature. 482(7385). 400–404. 932 indexed citations breakdown →
20.
Singh, Amarjit, et al.. (1990). Identification of virus(es) associated with chilli mosaic syndrome.. 27(1). 75–83. 1 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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