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
- Faizan Niazi (8 shared papers)Fei Huang (5 shared papers)Xia Han (3 shared papers)Hiroshi Yamazaki (2 shared papers)Tsutomu Shimada (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)Oral Oncology (3 papers)Clinical Pharmacology & Therapeutics (3 papers)Genes (3 papers)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Peter Shaw
111 papers receiving 3.3k citations
Peter Shaw's Hit Papers
Peers
Comparison fields: 5 of 163
- Pharmacology 560
- Cancer Research 479
- Oncology 791
- Hepatology 142
- Rheumatology 268
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 | 615 |
| 2 | 2007 | 274 | |
| 3 | 2011 | 236 | |
| 4 | 1998 | 169 | |
| 5 | 2018 | 131 | |
| 6 | 2011 | 126 | |
| 7 | 1997 | 120 | |
| 8 | 2004 | 117 | |
| 9 | 2018 | 106 | |
| 10 | 1997 | 80 | |
| 11 | 1985 | 71 | |
| 12 | 2017 | 64 | |
| 13 | 2005 | 62 | |
| 14 | 2011 | 60 | |
| 15 | 1991 | 52 | |
| 16 | 2006 | 51 | |
| 17 | 1989 | 46 | |
| 18 | 2018 | 46 | |
| 19 | 1996 | 46 | |
| 20 | 2007 | 43 |
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 3.4k indexed citations. Recurring topics across this work include Pharmacogenetics and Drug Metabolism (24 papers), Topic Modeling (7 papers), Drug Transport and Resistance Mechanisms (7 papers), MicroRNA in disease regulation (7 papers), Advanced Graph Theory Research (6 papers), Natural Language Processing Techniques (6 papers), Statistical Methods in Clinical Trials (5 papers) and Complexity and Algorithms in Graphs (5 papers). The work is most often cited by research in Pharmacology (560 citations), Cancer Research (479 citations), Oncology (791 citations), Hepatology (142 citations) and Rheumatology (268 citations). Peter Shaw has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Faizan Niazi, Fei Huang, Xia Han, Hiroshi Yamazaki, Tsutomu Shimada, F. Peter Guengerich, Mathew Nicholls, Roy D. Altman, Milton Adesnik and Ajay Manjoo. Their work appears in journals such as Pharmacogenomics, Archives of Biochemistry and Biophysics, Oral Oncology, Clinical Pharmacology & Therapeutics 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.