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.
Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning
2019519 citationsSiegfried Gessulat, Tobias Schmidt et al.Nature Methodsprofile →
Countries citing papers authored by Andreas Hühmer
Since
Specialization
Citations
This map shows the geographic impact of Andreas Hühmer'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 Andreas Hühmer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Hühmer more than expected).
This network shows the impact of papers produced by Andreas Hühmer. 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 Andreas Hühmer. The network helps show where Andreas Hühmer may publish in the future.
Co-authorship network of co-authors of Andreas Hühmer
This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Hühmer.
A scholar is included among the top collaborators of Andreas Hühmer 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 Andreas Hühmer. Andreas Hühmer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
4 of 4 papers shown
#
Work
Indexed citations
1
Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning 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.