Michael D. Waterfield

3.8k total citations · 3 hit papers
8 papers, 3.3k citations indexed

About

Michael D. Waterfield is a scholar working on Molecular Biology, Cell Biology and Oncology. According to data from OpenAlex, Michael D. Waterfield has authored 8 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 3 papers in Cell Biology and 1 paper in Oncology. Recurrent topics in Michael D. Waterfield's work include Protein Kinase Regulation and GTPase Signaling (7 papers), Cellular transport and secretion (3 papers) and PI3K/AKT/mTOR signaling in cancer (3 papers). Michael D. Waterfield is often cited by papers focused on Protein Kinase Regulation and GTPase Signaling (7 papers), Cellular transport and secretion (3 papers) and PI3K/AKT/mTOR signaling in cancer (3 papers). Michael D. Waterfield collaborates with scholars based in United Kingdom, Belgium and Australia. Michael D. Waterfield's co-authors include Bart Vanhaesebroeck, George Panayotou, Michael Fry, Ivan Gout, Ritu Dhand, Pablo Rodriguez‐Viciana, Patricia H. Warne, Julian Downward, Sally J. Leevers and Grant W. Booker and has published in prestigious journals such as Nature, Cell and Journal of Molecular Biology.

In The Last Decade

Michael D. Waterfield

8 papers receiving 3.2k citations

Hit Papers

Phosphatidylinositol-3-OH kinase direct target of Ras 1993 2026 2004 2015 1994 1997 1993 500 1000 1.5k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael D. Waterfield United Kingdom 8 2.6k 765 630 402 251 8 3.3k
Roy Katso United Kingdom 13 1.9k 0.7× 466 0.6× 409 0.6× 375 0.9× 298 1.2× 15 2.6k
Bart Vanhaesebroeck United Kingdom 16 2.7k 1.0× 588 0.8× 682 1.1× 652 1.6× 341 1.4× 17 3.8k
Anne B. Jefferson United States 21 2.2k 0.9× 634 0.8× 423 0.7× 366 0.9× 177 0.7× 26 3.0k
Ingrid Verlaan Netherlands 19 2.2k 0.9× 616 0.8× 383 0.6× 262 0.7× 97 0.4× 25 2.8k
Fuad Bahram Sweden 15 2.7k 1.0× 601 0.8× 917 1.5× 356 0.9× 99 0.4× 19 3.5k
Barbara Marte United States 19 2.3k 0.9× 484 0.6× 767 1.2× 361 0.9× 96 0.4× 36 3.0k
Laura Beguinot United States 27 2.5k 1.0× 903 1.2× 1.2k 1.9× 365 0.9× 100 0.4× 46 3.5k
Geraldine Mbamalu Canada 17 2.9k 1.1× 1.1k 1.5× 663 1.1× 360 0.9× 300 1.2× 20 4.0k
Joan Levy United States 28 2.6k 1.0× 545 0.7× 955 1.5× 487 1.2× 125 0.5× 58 3.6k
Maria Rozakis-Adcock Canada 22 2.2k 0.9× 663 0.9× 605 1.0× 330 0.8× 74 0.3× 23 3.0k

Countries citing papers authored by Michael D. Waterfield

Since Specialization
Citations

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

Fields of papers citing papers by Michael D. Waterfield

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael D. Waterfield

This figure shows the co-authorship network connecting the top 25 collaborators of Michael D. Waterfield. A scholar is included among the top collaborators of Michael D. Waterfield based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael D. Waterfield. Michael D. Waterfield is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
1.
O’Brien, Ronan, et al.. (2000). Alternative modes of binding of proteins with tandem SH2 domains. Protein Science. 9(3). 570–579. 30 indexed citations
2.
Siegal, Gregg, Ben Davis, Søren M. Kristensen, et al.. (1998). Solution structure of the C-terminal SH2 domain of the p85α regulatory subunit of phosphoinositide 3-kinase 1 1Edited by P. E. Wright. Journal of Molecular Biology. 276(2). 461–478. 48 indexed citations
3.
Vanhaesebroeck, Bart, Sally J. Leevers, George Panayotou, & Michael D. Waterfield. (1997). Phosphoinositide 3-kinases: A conserved family of signal transducers. Trends in Biochemical Sciences. 22(7). 267–272. 801 indexed citations breakdown →
4.
Kodaki, Tsutomu, et al.. (1994). A comparison of demethoxyviridin and wortmannin as inhibitors of phosphatidylinositol 3‐kinase. FEBS Letters. 342(2). 109–114. 107 indexed citations
5.
Rodriguez‐Viciana, Pablo, Patricia H. Warne, Ritu Dhand, et al.. (1994). Phosphatidylinositol-3-OH kinase direct target of Ras. Nature. 370(6490). 527–532. 1640 indexed citations breakdown →
6.
Panayotou, George & Michael D. Waterfield. (1993). The assembly of signalling complexes by receptor tyrosine kinases. BioEssays. 15(3). 171–177. 107 indexed citations
7.
Fry, Michael, George Panayotou, Grant W. Booker, & Michael D. Waterfield. (1993). New insights into protein‐tyrosine kinase receptor signaling complexes. Protein Science. 2(11). 1785–1797. 44 indexed citations
8.
Gout, Ivan, Ritu Dhand, Ian D. Hiles, et al.. (1993). The GTPase dynamin binds to and is activated by a subset of SH3 domains. Cell. 75(1). 25–36. 518 indexed citations breakdown →

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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