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.
Human insulin receptor and its relationship to the tyrosine kinase family of oncogenes
19851.9k citationsA Ullrich, Jeffrey R. Bell et al.Natureprofile →
Domain Interaction Between NMDA Receptor Subunits and the Postsynaptic Density Protein PSD-95
19951.6k citationsHans‐Christian Kornau, Leslie T. Schenker et al.Scienceprofile →
Relative abundance of subunit mRNAs determines gating and Ca2+ permeability of AMPA receptors in principal neurons and interneurons in rat CNS
19951.0k citationsJörg R. P. Geiger, Thorsten Melcher et al.Neuronprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of P. H. Seeburg'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 P. H. Seeburg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites P. H. Seeburg more than expected).
This network shows the impact of papers produced by P. H. Seeburg. 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 P. H. Seeburg. The network helps show where P. H. Seeburg may publish in the future.
Co-authorship network of co-authors of P. H. Seeburg
This figure shows the co-authorship network connecting the top 25 collaborators of P. H. Seeburg.
A scholar is included among the top collaborators of P. H. Seeburg 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 P. H. Seeburg. P. H. Seeburg is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Sanderson, David J., Mark Good, Kelly Skelton, et al.. (2009). Enhanced long-term and impaired short-term spatial memory in GluA1 AMPA receptor subunit knockout mice: Evidence for a dual-process memory model (vol 16, 379, 2009). Learning & Memory. 16. 508–508.1 indexed citations
Sprengel, Rolf, Miyoko Higuchi, Hannah Monyer, & P. H. Seeburg. (1999). Glutamate receptor channels: a possible link between RNA editing in the brain and epilepsy.. PubMed. 79. 525–34.11 indexed citations
Kornau, Hans‐Christian, Leslie T. Schenker, Mary B. Kennedy, & P. H. Seeburg. (1995). Domain Interaction Between NMDA Receptor Subunits and the Postsynaptic Density Protein PSD-95. Science. 269(5231). 1737–1740.1617 indexed citations breakdown →
Geiger, Jörg R. P., Thorsten Melcher, Duk-Su Koh, et al.. (1995). Relative abundance of subunit mRNAs determines gating and Ca2+ permeability of AMPA receptors in principal neurons and interneurons in rat CNS. Neuron. 15(1). 193–204.1030 indexed citations breakdown →
Verdoorn, Todd A., Robert S. Kass, P. H. Seeburg, & Bert Sakmann. (1990). Single channel properties of heterooligomeric rat gaba a receptors expressed using different alpha subunit variants. The Society for Neuroscience Abstracts. 16(1). 379.2 indexed citations
Ullrich, A, Jeffrey R. Bell, Román Herrera, et al.. (1985). Human insulin receptor and its relationship to the tyrosine kinase family of oncogenes. Nature. 313(6005). 756–761.1917 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.