Michael Shaw

798 citations
11 papers · 601 · h-index 7

Impact in

  • Genetics top 10%
    • Bacterial Genetics and Biotechnology
    • Extracellular vesicles in disease
    • Protein Structure and Dynamics
    • RNA and protein synthesis mechanisms
    • Prion Diseases and Protein Misfolding
    • Lipid Membrane Structure and Behavior

Papers in

    • Prion Diseases and Protein Misfolding 2
    • Bacterial biofilms and quorum sensing 1
    • Alzheimer's disease research and treatments 4

Michael Shaw

10 papers receiving 598 citations

Peers

Michael Shaw
Comparison fields: 5 of 80
  • Genetics 182
  • Molecular Biology 440
  • Endocrinology 23
  • Molecular Medicine 21
  • Neurology 31
Replace Dror S. Chorev with:
Dror S. Chorev United Kingdom
Jonathan D. Taylor United Kingdom
Kyoung‐Jae Choi United States
Edoardo D’Imprima Germany
Dongchun Ni Switzerland
Arfaan Rampersaud United States
Yuriy Chaban United Kingdom
Youzhong Guo United States
Micheal E. Barnett United States
Tomomi Kimura‐Someya Japan
Michael Shaw relative to Dror S. Chorev United Kingdom Dror S. Chorev's profile →
Citations per field
00.5×2.6×
Dror S. Chorev · 1×
Citations per year

Countries citing papers authored by Michael Shaw

Since Specialization
Citations

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

Fields of papers citing papers by Michael Shaw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

11 of 11 papers shown
#Work
1 2009253
2 2014142
3 201271
4 200947
5 200538
6 200724
7 200812
8 20156
9 20225
10 20223
11 20230

About Michael Shaw

Michael Shaw is a scholar working on Molecular Biology, Physiology, Pharmacology, Pulmonary and Respiratory Medicine and Computational Theory and Mathematics, having authored 11 papers that have together received 601 indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (4 papers), Cholinesterase and Neurodegenerative Diseases (2 papers), Computational Drug Discovery Methods (2 papers), Prion Diseases and Protein Misfolding (2 papers), Neurological diseases and metabolism (1 paper), COVID-19 diagnosis using AI (1 paper), Bacterial biofilms and quorum sensing (1 paper) and Trace Elements in Health (1 paper). The work is most often cited by research in Genetics (182 citations), Molecular Biology (440 citations), Endocrinology (23 citations), Molecular Medicine (21 citations) and Neurology (31 citations). Michael Shaw has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Leendert W. Hamoen, Jeff Errington, Davide Marenduzzo, Sven Halbedel, Ling Juan Wu, Rok Lenarčič, L. de Visser, Létitia Jean, David J. Vaux and Chiu Fan Lee. Their work appears in journals such as PLoS ONE, The EMBO Journal, BMJ Open, FEBS Letters and Journal of Biological Chemistry.

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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