Jeff Isaacson

3.4k citations
8 papers · 286 · h-index 6

Impact in

    • Ovarian cancer diagnosis and treatment
  • Oncology top 10%
    • PARP inhibition in cancer therapy
    • Pancreatic and Hepatic Oncology Research

Papers in

    • PARP inhibition in cancer therapy 7
    • Cancer therapeutics and mechanisms 1
    • CRISPR and Genetic Engineering 1

Jeff Isaacson

8 papers receiving 277 citations

Peers

Jeff Isaacson
Comparison fields: 5 of 36
  • Reproductive Medicine 83
  • Oncology 242
  • Physiology 11
  • Immunology 42
  • Molecular Biology 102
Replace William B. Ainsworth with:
William B. Ainsworth United States
Paul A. Ellis United States
C. Whalen United States
Larissa A. Sambel United States
E. Hitt Nichols United States
K.M. Bell-McGuinn United States
Francisca Reyes Turcu United States
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Lise M. van Wijk Netherlands
K Gugała Poland
Jeff Isaacson relative to William B. Ainsworth United States William B. Ainsworth's profile →
Citations per field
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William B. Ainsworth · 1×
Citations per year

Countries citing papers authored by Jeff Isaacson

Since Specialization
Citations

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

Fields of papers citing papers by Jeff Isaacson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jeff Isaacson, 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 Jeff Isaacson Line = papers co-authored together Jeff Isaacson links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 2017187
2 201929
3 201429
4 201819
5 201412
6 20235
7 20153
8 20152

About Jeff Isaacson

Jeff Isaacson is a scholar working on Oncology, Molecular Biology, Genetics, Immunology and Plant Science, having authored 8 papers that have together received 286 indexed citations. Recurring topics across this work include PARP inhibition in cancer therapy (7 papers), Cancer therapeutics and mechanisms (1 paper), Cancer Genomics and Diagnostics (1 paper), CRISPR and Genetic Engineering (1 paper), Plant Virus Research Studies (1 paper), BRCA gene mutations in cancer (1 paper) and Toxin Mechanisms and Immunotoxins (1 paper). The work is most often cited by research in Reproductive Medicine (83 citations), Oncology (242 citations), Physiology (11 citations), Immunology (42 citations) and Molecular Biology (102 citations). Jeff Isaacson has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Jim Xiao, Heidi Giordano, Lindsey Rolfe, Sandra Goble, Susan M. Domchek, Lara Maloney, Patricia LoRusso, Amit M. Oza, Manish R. Patel and Yvette Drew. Their work appears in journals such as Cancer Research, Journal of Clinical Oncology, Clinical Cancer Research, Investigational New Drugs and Clinical and Translational Science.

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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