Peter Shaw

111 papers receiving 3.3k citations

Hit Papers

Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts 2005 · 613 citations
6130+7+14Years since publication200400600

Peers

Peter Shaw
Comparison fields: 5 of 166
  • Pharmacology 582
  • Cancer Research 581
  • Oncology 935
  • Rheumatology 306
  • Molecular Biology 1.3k
Replace Ming Zheng with:
Ming Zheng United States
Shinya Sato Japan
Yingbin Liu China
Shinji Yamazaki United States
Laurent Corcos France
Yong Wu China
Takayuki Ikezoe Japan
Fritz F. Parl United States
Akira Tangoku Japan
Hua Xu China
Peter Shaw relative to Ming Zheng United States Ming Zheng's profile →
Citations per field
00.5×1.5×2.3×
Ming Zheng · 1×
Citations per year

Countries citing papers authored by Peter Shaw

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Peter Shaw Line = papers co-authored together Peter Shaw links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

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 →
2005613
2 2007274
3 2011233
4 1998167
5 2011126
6 2018124
7 1997120
8 2004116
9 2018101
10 199780
11 198571
12 201764
13 200562
14 201160
15 199152
16 200651
17 199646
18 198946
19 201845
20 200744

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.

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