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.
TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops
This map shows the geographic impact of Dirk Husmeier'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 Dirk Husmeier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dirk Husmeier more than expected).
This network shows the impact of papers produced by Dirk Husmeier. 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 Dirk Husmeier. The network helps show where Dirk Husmeier may publish in the future.
Co-authorship network of co-authors of Dirk Husmeier
This figure shows the co-authorship network connecting the top 25 collaborators of Dirk Husmeier.
A scholar is included among the top collaborators of Dirk Husmeier 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 Dirk Husmeier. Dirk Husmeier is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Niu, Mu, Simon Rogers, Maurizio Filippone, & Dirk Husmeier. (2016). Fast inference in nonlinear dynamical systems using gradient matching. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1699–1707.6 indexed citations
10.
Davies, Vinny, Richard Reeve, William T. Harvey, Francois F. Maree, & Dirk Husmeier. (2014). Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus. International Conference on Artificial Intelligence and Statistics. 149–158.4 indexed citations
11.
Husmeier, Dirk, et al.. (2013). Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 75–84.3 indexed citations
12.
Dondelinger, Frank, Dirk Husmeier, Simon Rogers, & Maurizio Filippone. (2013). ODE parameter inference using adaptive gradient matching with Gaussian processes. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 216–228.40 indexed citations
Lin, Kuang & Dirk Husmeier. (2010). Mixtures of factor analyzers for modeling transcriptional regulation. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam).1 indexed citations
15.
Grzegorczyk, Marco & Dirk Husmeier. (2009). Modelling non-stationary gene regulatory processes with a non-homogeneous dynamic Bayesian network and the change point process. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 56(1). 15–20.1 indexed citations
Werhli, Adriano Velasque, et al.. (2006). Improved gibbs sampling for detecting mosaic structures in DNA sequence alignments. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam).1 indexed citations
19.
Glasbey, C. A., Dirk Husmeier, Hans C. van Houwelingen, et al.. (2004). Clustering objects on subsets of attributes. Journal of the Royal Statistical Society Series B (Statistical Methodology). 66(4). 839–849.23 indexed citations
20.
Husmeier, Dirk, et al.. (1998). Modelling conditional probabilities with network committees: how overfitting can be useful. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam).2 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.