Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
AMP‐activated protein kinase phosphorylation of endothelial NO synthase
1999711 citationsDavid A. Power, Bruce E. Kemp et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by David A. Power
Since
Specialization
Citations
This map shows the geographic impact of David A. Power'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 David A. Power with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David A. Power more than expected).
This network shows the impact of papers produced by David A. Power. 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 David A. Power. The network helps show where David A. Power may publish in the future.
Co-authorship network of co-authors of David A. Power
This figure shows the co-authorship network connecting the top 25 collaborators of David A. Power.
A scholar is included among the top collaborators of David A. Power 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 David A. Power. David A. Power is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wolf, Jennifer Moriatis, Tobias R. Overbeck, Ji‐Youn Han, et al.. (2020). Capmatinib in patients with high-level MET-amplified advanced non-small cell lung cancer (NSCLC): results from the phase 2 GEOMETRY mono-1 study. Kölner Universitäts PublikationsServer (Universität zu Köln).8 indexed citations
Power, David A. & John H. Hanks. (2015). The Effect of Organic Acids, Serum Albumin, and Wetting Agents on Lag Phase, Dispersed Growth, and pH Stabilization in Mycobacterial Cultures1, 2. American Review of Respiratory Disease. 92(1). 83–93.3 indexed citations
Power, David A., et al.. (2000). Absence of immunohistochemical evidence for human herpesvirus 8 (HHV8) in oral cavity plasmablastic lymphoma in an HIV-positive man.. PubMed. 12(3). 194–194.27 indexed citations
MacLeod, Alison M., Robert J. Mason, David A. Power, et al.. (1987). Noncytotoxic Autoantibodies and Alloantibodies in Renal-Transplantation. Transplantation Proceedings. 17(2). 1771–1771.1 indexed citations
16.
Power, David A., Amanda Mather, Alison M. MacLeod, Thomas Lind, & G. R. D. Catto. (1986). Maternal antibodies to paternal B-lymphocytes in normal and abnormal pregnancy. American Journal of Reproductive Immunology. 10(1).1 indexed citations
17.
MacLeod, Alison M., G. R. D. Catto, Amanda Mather, et al.. (1985). BENEFICIAL ANTIBODIES IN RENAL-TRANSPLANTATION DEVELOPING AFTER BLOOD-TRANSFUSION - EVIDENCE FOR HLA LINKAGE. Transplantation Proceedings. 17(1). 1057–1058.3 indexed citations
MacLeod, Alison M., Amanda Mather, J Engeset, et al.. (1984). SELECTION OF RENAL-TRANSPLANT RECIPIENTS USING THE EA INHIBITION ASSAY - A FURTHER EVALUATION. Kidney International. 26(4). 644–644.1 indexed citations
20.
Power, David A., Alison M. MacLeod, Robert J. Mason, et al.. (1982). Lymphocyte-B antibodies in successful renal-transplantation and successful pregnancy. Kidney International. 22(5). 582–583.1 indexed citations
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.