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.
Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega
201111.2k citationsFabian Sievers, Andreas Wilm et al.Molecular Systems Biologyprofile →
Improving physical realism, stereochemistry, and side‐chain accuracy in homology modeling: Four approaches that performed well in CASP8
20091.1k citationsElmar Krieger, Keehyoung Joo et al.Proteins Structure Function and Bioinformaticsprofile →
Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure
2001962 citationsJulian Gough, Kevin Karplus et al.profile →
Hidden Markov models for detecting remote protein homologies.
1998861 citationsKevin Karplus, Richard Hughey et al.Bioinformaticsprofile →
Automated forward and reverse ratcheting of DNA in a nanopore at 5-Å precision
This map shows the geographic impact of Kevin Karplus'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 Kevin Karplus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Karplus more than expected).
This network shows the impact of papers produced by Kevin Karplus. 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 Kevin Karplus. The network helps show where Kevin Karplus may publish in the future.
Co-authorship network of co-authors of Kevin Karplus
This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Karplus.
A scholar is included among the top collaborators of Kevin Karplus 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 Kevin Karplus. Kevin Karplus is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sievers, Fabian, Andreas Wilm, Toby J. Gibson, et al.. (2011). Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega. Molecular Systems Biology. 7(1). 539–539.11202 indexed citations breakdown →
Krieger, Elmar, Keehyoung Joo, Jinwoo Lee, et al.. (2009). Improving physical realism, stereochemistry, and side‐chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins Structure Function and Bioinformatics. 77(S9). 114–122.1107 indexed citations breakdown →
Karplus, Kevin, et al.. (1998). Kestrel: A Programmable Array for Sequence Analysis. The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology. 19(2). 115–126.28 indexed citations
13.
Gaasterland, Terry, Peter D. Karp, Kevin Karplus, et al.. (1997). Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology.43 indexed citations
14.
Bücher, Philipp, et al.. (1996). A flexible search technique based on generalized profiles.. IRIS.24 indexed citations
15.
Karplus, Kevin. (1994). USING MARKOV MODELS AND HIDDEN MARKOV MODELS TO FIND REPETITIVE EXTRAGENIC PALINDROMIC SEQUENCES IN ESCHERICHIA COLI. 104(2703). 376–376.3 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.