Chris A. Learn

888 citations
9 papers · 716 · h-index 7

Impact in

  • Genetics top 5%
    • Glioma Diagnosis and Treatment
  • Immunology top 10%
    • Immunotherapy and Immune Responses

Papers in

    • HER2/EGFR in Cancer Research 2
    • Cancer Immunotherapy and Biomarkers 1
    • Cancer Treatment and Pharmacology 1
    • Immune Response and Inflammation 2
    • Immune Cell Function and Interaction 2

Chris A. Learn

9 papers receiving 698 citations

Peers

Chris A. Learn
Comparison fields: 5 of 66
  • Genetics 162
  • Immunology 246
  • Oncology 311
  • Epidemiology 209
  • Cancer Research 79
Replace Ursula R. Sorg with:
Ursula R. Sorg Germany
Anna Koutoulaki Greece
Muneer G. Hasham United States
Masashi Sakaki Japan
Yasuhiko Kamikubo Japan
Jaring Schreuder Australia
Eric Burton United States
Kito Nzingha United States
Jérôme Mastio United States
Noriyasu Seki Japan
Chris A. Learn relative to Ursula R. Sorg Germany Ursula R. Sorg's profile →
Citations per field
00.5×3.3×
Ursula R. Sorg · 1×
Citations per year

Countries citing papers authored by Chris A. Learn

Since Specialization
Citations

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

Fields of papers citing papers by Chris A. Learn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chris A. Learn. 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 Chris A. Learn. The network helps show where Chris A. Learn may publish in the future.

Co-authors

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

All Works

9 of 9 papers shown
#Work
1 2007311
2
Brain tumors in mice are susceptible to blockade of epidermal growth factor receptor (EGFR) with the oral, specific, EGFR-tyrosine kinase inhibitor ZD1839 (iressa).
2002138
3 2004131
4 200653
5 200044
6 200128
7 20066
8 20253
9 20232

About Chris A. Learn

Chris A. Learn is a scholar working on Oncology, Immunology, Molecular Biology, Epidemiology and Pharmacology, having authored 9 papers that have together received 716 indexed citations. Recurring topics across this work include Immune Response and Inflammation (2 papers), HER2/EGFR in Cancer Research (2 papers), Immune Cell Function and Interaction (2 papers), Cancer Immunotherapy and Biomarkers (1 paper), MicroRNA in disease regulation (1 paper), Cancer Treatment and Pharmacology (1 paper), Cytomegalovirus and herpesvirus research (1 paper) and Computational Drug Discovery Methods (1 paper). The work is most often cited by research in Genetics (162 citations), Immunology (246 citations), Oncology (311 citations), Epidemiology (209 citations) and Cancer Research (79 citations). Chris A. Learn has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include John H. Sampson, Allan H. Friedman, Roger E. McLendon, Robert J. Schmittling, Weihua Xie, Duane A. Mitchell, Gary E. Archer, Darell D. Bigner, Henry S. Friedman and Charles E. McCall. Their work appears in journals such as Clinical Cancer Research, Journal of Biological Chemistry, Journal of Neuroimmunology, Journal of Clinical Oncology and Neuro-Oncology.

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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