Susan M. Wade

41 total papers · 1.2k total citations
30 papers, 1.0k citations indexed

About

Susan M. Wade is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Susan M. Wade has authored 30 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 9 papers in Cellular and Molecular Neuroscience and 3 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Susan M. Wade's work include Receptor Mechanisms and Signaling (17 papers), Protein Kinase Regulation and GTPase Signaling (9 papers) and Neuropeptides and Animal Physiology (7 papers). Susan M. Wade is often cited by papers focused on Receptor Mechanisms and Signaling (17 papers), Protein Kinase Regulation and GTPase Signaling (9 papers) and Neuropeptides and Animal Physiology (7 papers). Susan M. Wade collaborates with scholars based in United States, Japan and Gabon. Susan M. Wade's co-authors include Richard R. Neubig, Chris R. Evelyn, Jennifer J. Linderman, Mei Wu, Qin Wang, Sofía D. Merajver, Jorge A. Iñiguez‐Lluhí, Jessica L. Bell, Scott D. Larsen and John R. Traynor and has published in prestigious journals such as Journal of Biological Chemistry, SHILAP Revista de lepidopterología and Analytical Chemistry.

In The Last Decade

Susan M. Wade

30 papers receiving 998 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Susan M. Wade 766 277 129 85 75 30 1.0k
Joan M. Lyles 542 0.7× 246 0.9× 136 1.1× 42 0.5× 48 0.6× 20 1.0k
Negin P. Martin 861 1.1× 287 1.0× 204 1.6× 61 0.7× 62 0.8× 40 1.2k
Steven Yu 680 0.9× 340 1.2× 86 0.7× 169 2.0× 48 0.6× 30 1.1k
Katalin Pászty 818 1.1× 169 0.6× 232 1.8× 51 0.6× 93 1.2× 41 1.1k
J. H. Steinbach 605 0.8× 370 1.3× 129 1.0× 78 0.9× 32 0.4× 17 988
Jürgen Reinhardt 700 0.9× 179 0.6× 101 0.8× 123 1.4× 87 1.2× 26 1.0k
Carsten Kummerow 606 0.8× 201 0.7× 110 0.9× 93 1.1× 35 0.5× 17 1.2k
Walter Strapps 713 0.9× 212 0.8× 99 0.8× 74 0.9× 27 0.4× 13 965
Lawrence Baizer 542 0.7× 311 1.1× 155 1.2× 110 1.3× 29 0.4× 28 1.0k
Leise A. Berven 609 0.8× 166 0.6× 254 2.0× 77 0.9× 29 0.4× 23 908

Countries citing papers authored by Susan M. Wade

Since Specialization
Citations

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

Fields of papers citing papers by Susan M. Wade

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Susan M. Wade. 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 Susan M. Wade. The network helps show where Susan M. Wade may publish in the future.

Co-authorship network of co-authors of Susan M. Wade

This figure shows the co-authorship network connecting the top 25 collaborators of Susan M. Wade. A scholar is included among the top collaborators of Susan M. Wade 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 Susan M. Wade. Susan M. Wade is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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