Iris T. Chan
Impact in
- Oncology top 5%
- Cutaneous Melanoma Detection and Management
- HER2/EGFR in Cancer Research
- Hematology top 5%
- Acute Myeloid Leukemia Research
Papers in
-
- Melanoma and MAPK Pathways 9
- PI3K/AKT/mTOR signaling in cancer 5
-
- Advanced Breast Cancer Therapies 6
- Co-authors
- Luna Musib (10 shared papers)Leisa Johnson (2 shared papers)Ifor R. Williams (2 shared papers)Tyler Jacks (2 shared papers)Hirokazu Shigematsu (2 shared papers)David A. Tuveson (2 shared papers)Jeffery L. Kutok (2 shared papers)Koichi Akashi (2 shared papers)
- Journals
- Cancer Research (5 papers)Investigational New Drugs (3 papers)Molecular Cancer Therapeutics (2 papers)Journal of Clinical Investigation (2 papers)Journal of Clinical Oncology (2 papers)
- Partner nations
- United StatesUnited KingdomAustralia
In The Last Decade
Iris T. Chan
28 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 73
- Oncology 547
- Hematology 187
- Cancer Research 199
- Molecular Biology 725
- Genetics 104
Countries citing papers authored by Iris T. Chan
This map shows the geographic impact of Iris T. Chan'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 Iris T. Chan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Iris T. Chan more than expected).
Fields of papers citing papers by Iris T. Chan
This network shows the impact of papers produced by Iris T. Chan. 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 Iris T. Chan. The network helps show where Iris T. Chan may publish in the future.
Co-authors
The 25 scholars most cited alongside Iris T. Chan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 219 | |
| 2 | 2004 | 205 | |
| 3 | 2014 | 201 | |
| 4 | 2019 | 73 | |
| 5 | 2016 | 62 | |
| 6 | 1993 | 58 | |
| 7 | 2012 | 42 | |
| 8 | 2012 | 42 | |
| 9 | 2011 | 40 | |
| 10 | 2006 | 38 | |
| 11 | 2013 | 36 | |
| 12 | 2019 | 28 | |
| 13 | 2020 | 23 | |
| 14 | 2015 | 21 | |
| 15 | 2004 | 21 | |
| 16 | 2011 | 18 | |
| 17 | 2012 | 16 | |
| 18 | 2022 | 14 | |
| 19 | 2012 | 13 | |
| 20 | 2011 | 11 |
About Iris T. Chan
Iris T. Chan is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine, Oncology, Genetics and Cancer Research, having authored 29 papers that have together received 1.2k indexed citations. Recurring topics across this work include Melanoma and MAPK Pathways (9 papers), Advanced Breast Cancer Therapies (6 papers), PI3K/AKT/mTOR signaling in cancer (5 papers), Estrogen and related hormone effects (4 papers), Colorectal Cancer Treatments and Studies (3 papers), Cancer Treatment and Pharmacology (3 papers), Acute Myeloid Leukemia Research (3 papers) and Cancer Genomics and Diagnostics (3 papers). The work is most often cited by research in Oncology (547 citations), Hematology (187 citations), Cancer Research (199 citations), Molecular Biology (725 citations) and Genetics (104 citations). Iris T. Chan has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Luna Musib, Leisa Johnson, Ifor R. Williams, Tyler Jacks, Hirokazu Shigematsu, David A. Tuveson, Jeffery L. Kutok, Koichi Akashi, Lauren S. Kelly and Sarah L. Cohen. Their work appears in journals such as Cancer Research, Investigational New Drugs, Molecular Cancer Therapeutics, Journal of Clinical Investigation and Journal of Clinical 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.