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.
Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning
2022140 citationsCaroline Weis, Aline Cuénod et al.Nature Medicineprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Susanne Graf'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 Susanne Graf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Susanne Graf more than expected).
This network shows the impact of papers produced by Susanne Graf. 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 Susanne Graf. The network helps show where Susanne Graf may publish in the future.
Co-authorship network of co-authors of Susanne Graf
This figure shows the co-authorship network connecting the top 25 collaborators of Susanne Graf.
A scholar is included among the top collaborators of Susanne Graf 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 Susanne Graf. Susanne Graf 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.
Weis, Caroline, Aline Cuénod, Bastian Rieck, et al.. (2022). Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. Nature Medicine. 28(1). 164–174.140 indexed citations breakdown →
Boer, Frank S. de, et al.. (2006). Formal methods for components and objects : 4th international symposium, FMCO 2005, Amsterdam, The Netherlands, November 1-4, 2005, revised lectures. Springer eBooks.1 indexed citations
10.
Bonsangue, Marcello, et al.. (2005). Proceedings of the Third International Symposium on Formal Methods for Components and Objects. Lecture notes in computer science. 3657. 1–333.2 indexed citations
11.
Graf, Susanne & Andreas Prinz. (2005). Time in State Machines.. HAL (Le Centre pour la Communication Scientifique Directe). 217–232.1 indexed citations
12.
Boer, Frank S. de, Marcello Bonsangue, Susanne Graf, & Willem-Paul de Roever. (2004). Formal Methods for Components and Objects: Second International Symposium, FMCO 2003, Leiden, The Netherlands, November 4-7, 2003. Revised Lectures (Lecture Notes in Computer Science). Springer eBooks.5 indexed citations
13.
Boer, Frank S. de, Marcello Bonsangue, Susanne Graf, & Willem-Paul de Roever. (2004). Proceedings of the Third international conference on Formal Methods for Components and Objects. Formal Methods.1 indexed citations
Brinksma, Ed, Jim Davies, Rob Gerth, et al.. (1994). Verifying sequentially consistent memory. Data Archiving and Networked Services (DANS). 9444.2 indexed citations
20.
Baptista, Márcia L., Susanne Graf, Jean-Luc Richier, et al.. (1990). Formal Specification and Verification of a Network Independent Atomic Multicast Protocol. 345–352.6 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.