Peter Shaw
Impact in
- Pharmacology top 0.5%
- Pharmacogenetics and Drug Metabolism
- Cancer Research top 5%
- Cancer Genomics and Diagnostics
- MicroRNA in disease regulation
Papers in ⓘ
- Pharmacology 25
- Pharmacogenetics and Drug Metabolism 24
- Co-authors
- Fei Huang (5 shared papers)Faizan Niazi (8 shared papers)Xia Han (3 shared papers)Tsutomu Shimada (2 shared papers)Hiroshi Yamazaki (2 shared papers)F. Peter Guengerich (2 shared papers)Mathew Nicholls (5 shared papers)Roy D. Altman (2 shared papers)
- Journals
- Pharmacogenomics (5 papers)Archives of Biochemistry and Biophysics (3 papers)Clinical Pharmacology & Therapeutics (3 papers)Oral Oncology (3 papers)Genes (3 papers)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Peter Shaw
111 papers receiving 3.3k citations
Hit Papers
Peers
Comparison fields: 5 of 166
- Pharmacology 582
- Cancer Research 581
- Oncology 935
- Rheumatology 306
- Molecular Biology 1.3k
Countries citing papers authored by Peter Shaw
This map shows the geographic impact of Peter Shaw'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 Peter Shaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Shaw more than expected).
Fields of papers citing papers by Peter Shaw
This network shows the impact of papers produced by Peter Shaw. 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 Peter Shaw. The network helps show where Peter Shaw may publish in the future.
Co-authors
The 25 scholars most cited alongside Peter Shaw, 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 117 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts Hit paper breakdown → | 2005 | 613 |
| 2 | 2007 | 274 | |
| 3 | 2011 | 233 | |
| 4 | 1998 | 167 | |
| 5 | 2011 | 126 | |
| 6 | 2018 | 124 | |
| 7 | 1997 | 120 | |
| 8 | 2004 | 116 | |
| 9 | 2018 | 101 | |
| 10 | 1997 | 80 | |
| 11 | 1985 | 71 | |
| 12 | 2017 | 64 | |
| 13 | 2005 | 62 | |
| 14 | 2011 | 60 | |
| 15 | 1991 | 52 | |
| 16 | 2006 | 51 | |
| 17 | 1996 | 46 | |
| 18 | 1989 | 46 | |
| 19 | 2018 | 45 | |
| 20 | 2007 | 44 |
About Peter Shaw
Peter Shaw is a scholar working on Molecular Biology, Pharmacology, Cancer Research, Oncology and Artificial Intelligence, having authored 117 papers that have together received 3.3k indexed citations. Recurring topics across this work include Pharmacogenetics and Drug Metabolism (24 papers), Osteoarthritis Treatment and Mechanisms (8 papers), Drug Transport and Resistance Mechanisms (7 papers), Topic Modeling (7 papers), MicroRNA in disease regulation (7 papers), Natural Language Processing Techniques (6 papers), Advanced Graph Theory Research (6 papers) and Cancer-related molecular mechanisms research (6 papers). The work is most often cited by research in Pharmacology (582 citations), Cancer Research (581 citations), Oncology (935 citations), Rheumatology (306 citations) and Molecular Biology (1.3k citations). Peter Shaw has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Fei Huang, Faizan Niazi, Xia Han, Tsutomu Shimada, Hiroshi Yamazaki, F. Peter Guengerich, Mathew Nicholls, Roy D. Altman, Milton Adesnik and Yudi Pawitan. Their work appears in journals such as Pharmacogenomics, Archives of Biochemistry and Biophysics, Clinical Pharmacology & Therapeutics, Oral Oncology and Genes.
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.