Peter Shaw
Impact in
- Pharmacology top 0.5%
- Pharmacogenetics and Drug Metabolism
- Cancer Research top 5%
- Cancer Genomics and Diagnostics
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)Oral Oncology (3 papers)Archives of Biochemistry and Biophysics (3 papers)Clinical Pharmacology & Therapeutics (3 papers)Genes (3 papers)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Peter Shaw
120 papers receiving 3.6k citations
Peter Shaw's Hit Papers
Peers
Comparison fields: 5 of 167
- Pharmacology 597
- Cancer Research 488
- Oncology 808
- Hepatology 153
- Rheumatology 274
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 127 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 | 632 |
| 2 | 2007 | 279 | |
| 3 | 2011 | 236 | |
| 4 | 1998 | 171 | |
| 5 | 2018 | 134 | |
| 6 | 2011 | 127 | |
| 7 | 1997 | 121 | |
| 8 | 2004 | 120 | |
| 9 | 2018 | 109 | |
| 10 | 1997 | 81 | |
| 11 | 1985 | 75 | |
| 12 | 2011 | 70 | |
| 13 | 2017 | 66 | |
| 14 | 2005 | 62 | |
| 15 | 1991 | 53 | |
| 16 | 2006 | 52 | |
| 17 | 1989 | 47 | |
| 18 | 2018 | 47 | |
| 19 | 1996 | 47 | |
| 20 | 2007 | 45 |
About Peter Shaw
Peter Shaw is a scholar working on Molecular Biology, Pharmacology, Computational Theory and Mathematics, Cancer Research and Oncology, having authored 127 papers that have together received 3.7k indexed citations. Recurring topics across this work include Pharmacogenetics and Drug Metabolism (24 papers), Advanced Graph Theory Research (13 papers), Complexity and Algorithms in Graphs (9 papers), Topic Modeling (7 papers), Drug Transport and Resistance Mechanisms (7 papers), MicroRNA in disease regulation (7 papers), Natural Language Processing Techniques (6 papers) and Optimization and Search Problems (5 papers). The work is most often cited by research in Pharmacology (597 citations), Cancer Research (488 citations), Oncology (808 citations), Hepatology (153 citations) and Rheumatology (274 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, Oral Oncology, Archives of Biochemistry and Biophysics, 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.