Ken Garber

5.0k citations
178 papers · 3.9k · h-index 31

Impact in

Papers in

    • Protein Degradation and Inhibitors 10
    • CAR-T cell therapy research 13
    • Cancer Immunotherapy and Biomarkers 11

Ken Garber

178 papers receiving 3.7k citations

Peers

Ken Garber
Comparison fields: 5 of 147
  • Oncology 1.1k
  • Cancer Research 520
  • Molecular Biology 2.0k
  • Immunology 518
  • Genetics 606
Replace Marco G. Paggi with:
Marco G. Paggi Italy
Amy Lin United States
Ameeta Kelekar United States
Gennadi V. Glinsky United States
Ji‐Ying Song Netherlands
Amanda McCann Ireland
Christine Sers Germany
Emily I. Chen United States
Shensi Shen France
Kian‐Huat Lim United States
Ken Garber relative to Marco G. Paggi Italy Marco G. Paggi's profile →
Citations per field
00.5×1.5×1.8×
Marco G. Paggi · 1×
Citations per year

Countries citing papers authored by Ken Garber

Since Specialization
Citations

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

Fields of papers citing papers by Ken Garber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Ken Garber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ken Garber Line = papers co-authored together Ken Garber links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 178 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2006373
2 2006231
3 2015142
4 2018136
5 2021131
6 2018128
7 2014102
8 200889
9 200778
10 200777
11 201854
12 201552
13 201051
14 200649
15 201445
16 201445
17 201645
18 200244
19 201643
20 200941

About Ken Garber

Ken Garber is a scholar working on Molecular Biology, Oncology, Immunology, Cancer Research and Radiology, Nuclear Medicine and Imaging, having authored 178 papers that have together received 3.9k indexed citations. Recurring topics across this work include Science, Research, and Medicine (14 papers), Monoclonal and Polyclonal Antibodies Research (13 papers), CAR-T cell therapy research (13 papers), Cancer Immunotherapy and Biomarkers (11 papers), Protein Degradation and Inhibitors (10 papers), Immunotherapy and Immune Responses (9 papers), Biomedical Ethics and Regulation (9 papers) and Cancer Genomics and Diagnostics (9 papers). The work is most often cited by research in Oncology (1.1k citations), Cancer Research (520 citations), Molecular Biology (2.0k citations), Immunology (518 citations) and Genetics (606 citations). Ken Garber has collaborated with scholars based in United States, Germany and Poland. Frequent co-authors include Lin Li, Chadwick M. Hales, Laura DeFrancesco, Robert Guderian, Esther Landhuis, Michael Eisenstein, Cormac Sheridan, Daniel Clery, Melanie Senior and Jocelyn Kaiser. Their work appears in journals such as Nature Biotechnology, JNCI Journal of the National Cancer Institute, Science, Nature Reviews Drug Discovery and Nature.

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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