Steve Rees

969 citations
9 papers · 495 · 1 hit paper · h-index 6

Impact in

Papers in

Steve Rees

7 papers receiving 479 citations

Steve Rees's Hit Papers

Impact of a five-dimensional framework on R&D productivity at AstraZeneca 2018 · 271 citations
2710+2+5Years since publication50100150200250

Peers

Steve Rees
Comparison fields: 5 of 102
  • Computational Theory and Mathematics 87
  • Cellular and Molecular Neuroscience 97
  • Molecular Biology 257
  • Pharmacology 29
  • Biophysics 19
Replace Susan M. Mockus with:
Susan M. Mockus United States
Ian L. Dale United Kingdom
Silvia Avino Italy
Daju Fan United States
Deniz Kahraman Germany
Izumi V. Hinkson United States
Gavin C. Hirst United States
Alexey Goltsov United Kingdom
Adebayo Laniyonu Canada
Haibei Hu United States
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Citations per field
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Citations per year

Countries citing papers authored by Steve Rees

Since Specialization
Citations

This map shows the geographic impact of Steve Rees'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 Steve Rees with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steve Rees more than expected).

Fields of papers citing papers by Steve Rees

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Steve Rees. 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 Steve Rees. The network helps show where Steve Rees may publish in the future.

Co-authors

The 25 scholars most cited alongside Steve Rees, 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 Steve Rees Line = papers co-authored together Steve Rees links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1
Impact of a five-dimensional framework on R&D productivity at AstraZeneca
Hit paper breakdown →
2018271
2 2013150
3 199931
4 201521
5 201212
6 20226
7 20154
8 20250
9 20250

About Steve Rees

Steve Rees is a scholar working on Molecular Biology, Computational Theory and Mathematics, Cellular and Molecular Neuroscience, Cardiology and Cardiovascular Medicine and Ocean Engineering, having authored 9 papers that have together received 495 indexed citations. Recurring topics across this work include CRISPR and Genetic Engineering (4 papers), Computational Drug Discovery Methods (3 papers), RNA and protein synthesis mechanisms (2 papers), Neuropeptides and Animal Physiology (2 papers), RNA Interference and Gene Delivery (2 papers), Receptor Mechanisms and Signaling (2 papers), Oil and Gas Production Techniques (1 paper) and Reservoir Engineering and Simulation Methods (1 paper). The work is most often cited by research in Computational Theory and Mathematics (87 citations), Cellular and Molecular Neuroscience (97 citations), Molecular Biology (257 citations), Pharmacology (29 citations) and Biophysics (19 citations). Steve Rees has collaborated with scholars based in United Kingdom, United States and Sweden. Frequent co-authors include Mark J. Anderton, Ruth March, Stefan Platz, Dean G. Brown, Jerome T. Mettetal, James Matcham, Paul Morgan, Michael Snowden, Simon Lennard and Bengt Hamrén. Their work appears in journals such as SLAS DISCOVERY, Nature Reviews Drug Discovery, British Journal of Pharmacology and Frontiers in Pharmacology.

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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