Dirk Husmeier

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 24

Top Topics

Differential Equations

Hierarchical Bayesian

Gaussian Process

Model Selection

Top Venues

Publications

Yalei Yang, Dirk Husmeier, Hao Gao, Colin Berry, David Carrick, Aleksandra Radjenovic
Automatic detection of myocardial ischaemia using generalisable spatio-temporal hierarchical Bayesian modelling of DCE-MRI. Comput. Medical Imaging Graph. 113 (2024)
Jianmei Zhou, Dirk Husmeier, Hao Gao, Changchun Yin, Changkai Qiu, Xu Jing, Yanfu Qi, Wentao Liu
Bayesian Inversion of Frequency-Domain Airborne EM Data With Spatial Correlation Prior Information. IEEE Trans. Geosci. Remote. Sens. 62 (2024)
William T. Harvey, Vinny Davies, Rodney S. Daniels, Lynne Whittaker, Victoria Gregory, Alan J. Hay, Dirk Husmeier, John W. McCauley, Richard E. Reeve
A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. PLoS Comput. Biol. 19 (3) (2023)
Ionut Paun, Dirk Husmeier, Colin J. Torney
Stochastic variational inference for scalable non-stationary Gaussian process regression. Stat. Comput. 33 (2) (2023)
Arash Rabbani, Hao Gao, Alan Lazarus, David Dalton, Yuzhang Ge, Kenneth Mangion, Colin Berry, Dirk Husmeier
Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications. Comput. Medical Imaging Graph. 106 (2023)
Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith, Dirk Husmeier
The Fully Convolutional Transformer for Medical Image Segmentation. WACV (2023)
Arash Rabbani, Hao Gao, Dirk Husmeier
Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking. CoRR (2022)
Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith, Dirk Husmeier
The Fully Convolutional Transformer for Medical Image Segmentation. CoRR (2022)
Luiza Mihaela Paun, Dirk Husmeier
Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models. Stat. Comput. 32 (1) (2022)
Shaykhah Aldossari, Dirk Husmeier, Jason Matthiopoulos
Transferable species distribution modelling: Comparative performance of Generalised Functional Response models. Ecol. Informatics 71 (2022)
Mu Niu, Joe Wandy, Rónán Daly, Simon Rogers, Dirk Husmeier
R package for statistical inference in dynamical systems using kernel based gradient matching: KGode. Comput. Stat. 36 (1) (2021)
Lukasz Romaszko, Agnieszka Borowska, Alan Lazarus, David Dalton, Colin Berry, Xiaoyu Luo, Dirk Husmeier, Hao Gao
Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. Artif. Intell. Medicine 119 (2021)
Agnieszka Borowska, Diana Giurghita, Dirk Husmeier
Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis. J. Comput. Phys. 429 (2021)
Benn Macdonald, Dirk Husmeier
Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching. Stat. Comput. 29 (5) (2019)
Mu Niu, Benn Macdonald, Simon Rogers, Maurizio Filippone, Dirk Husmeier
Statistical inference in mechanistic models: time warping for improved gradient matching. Comput. Stat. 33 (2) (2018)
Joe Wandy, Mu Niu, Diana Giurghita, Rónán Daly, Simon Rogers, Dirk Husmeier
ShinyKGode: an interactive application for ODE parameter inference using gradient matching. Bioinform. 34 (13) (2018)
Alan Lazarus, Dirk Husmeier, Theodore Papamarkou
Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations. AISTATS (2018)
Umberto Noè, Dirk Husmeier
On a New Improvement-Based Acquisition Function for Bayesian Optimization. CoRR (2018)
Vinny Davies, Richard Reeve, William T. Harvey, Francois F. Maree, Dirk Husmeier
A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution. Comput. Stat. 32 (3) (2017)
Marco Grzegorczyk, Andrej Aderhold, Dirk Husmeier
Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration. Comput. Stat. 32 (2) (2017)
Andrej Aderhold, Dirk Husmeier, Marco Grzegorczyk
Approximate Bayesian inference in semi-mechanistic models. Stat. Comput. 27 (4) (2017)
Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching. ICML (2016)
Umberto Noè, Weiwei Chen, Maurizio Filippone, Nicholas Hill, Dirk Husmeier
Inference in a Partial Differential Equations Model of Pulmonary Arterial and Venous Blood Circulation Using Statistical Emulation. CIBB (2016)
Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier
Parameter Inference in Differential Equation Models of Biopathways Using Time Warped Gradient Matching. CIBB (2016)
Catherine F. Higham, Dirk Husmeier
Inference of Circadian Regulatory Pathways Based on Delay Differential Equations. IWBBIO (2) (2015)
Vinny Davies, Richard Reeve, William T. Harvey, Dirk Husmeier
Selecting Random Effect Components in a Sparse Hierarchical Bayesian Model for Identifying Antigenic Variability. CIBB (2015)
Benn Macdonald, Catherine F. Higham, Dirk Husmeier
Controversy in mechanistic modelling with Gaussian processes. ICML (2015)
Benn Macdonald, Dirk Husmeier
Computational Inference in Systems Biology. IWBBIO (2) (2015)
Vinny Davies, Richard Reeve, William T. Harvey, Francois F. Maree, Dirk Husmeier
Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus. AISTATS (2014)
Marco Grzegorczyk, Andrej Aderhold, V. Anne Smith, Dirk Husmeier
Inference of Circadian Regulatory Networks. IWBBIO (2014)
Frank Dondelinger, Sophie Lèbre, Dirk Husmeier
Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Mach. Learn. 90 (2) (2013)
Marco Grzegorczyk, Dirk Husmeier
Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models. Mach. Learn. 91 (1) (2013)
Andrej Aderhold, Dirk Husmeier, V. Anne Smith
Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes. AISTATS (2013)
Catherine F. Higham, Dirk Husmeier
A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana. BMC Bioinform. 14 (S-10) (2013)
Frank Dondelinger, Dirk Husmeier, Simon Rogers, Maurizio Filippone
ODE parameter inference using adaptive gradient matching with Gaussian processes. AISTATS (2013)
Andrej Aderhold, Dirk Husmeier, Jack J. Lennon, Colin M. Beale, V. Anne Smith
Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data. Ecol. Informatics 11 (2012)
Marco Grzegorczyk, Dirk Husmeier
Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters. AISTATS (2012)
Marco Grzegorczyk, Dirk Husmeier
Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes. Bioinform. 27 (5) (2011)
Marco Grzegorczyk, Dirk Husmeier
Non-homogeneous dynamic Bayesian networks for continuous data. Mach. Learn. 83 (3) (2011)
Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer
Modelling non-stationary dynamic gene regulatory processes with the BGM model. Comput. Stat. 26 (2) (2011)
Ali Faisal, Frank Dondelinger, Dirk Husmeier, Colin M. Beale
Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods. Ecol. Informatics 5 (6) (2010)
Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer
Modelling Nonstationary Gene Regulatory Processes. Adv. Bioinformatics 2010 (2010)
Dirk Husmeier, Frank Dondelinger, Sophie Lèbre
Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks. NIPS (2010)
Frank Dondelinger, Sophie Lèbre, Dirk Husmeier
Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing. ICML (2010)
Alexander V. Mantzaris, Dirk Husmeier
Distinguishing Regional from Within-Codon Rate Heterogeneity in DNA Sequence Alignments. PRIB (2009)
Marco Grzegorczyk, Dirk Husmeier
Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks. PRIB (2009)
Marco Grzegorczyk, Dirk Husmeier
Non-stationary continuous dynamic Bayesian networks. NIPS (2009)
Kuang Lin, Dirk Husmeier
Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization. EURASIP J. Bioinform. Syst. Biol. 2009 (2009)
Iain Milne, Dominik Lindner, Micha Bayer, Dirk Husmeier, Gráinne McGuire, David F. Marshall, Frank Wright
TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops. Bioinform. 25 (1) (2009)
Marco Grzegorczyk, Dirk Husmeier, Kieron D. Edwards, Peter Ghazal, Andrew J. Millar
Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler. Bioinform. 24 (18) (2008)
Adriano Velasque Werhli, Dirk Husmeier
Gene Regulatory Network Reconstruction by Bayesian Integration of Prior Knowledge and/or Different Experimental Conditions. J. Bioinform. Comput. Biol. 6 (3) (2008)
Marco Grzegorczyk, Dirk Husmeier
Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Mach. Learn. 71 (2-3) (2008)
Adriano Velasque Werhli, Marco Grzegorczyk, Dirk Husmeier
Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinform. 22 (20) (2006)
Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams
A regularized discriminative model for the prediction of protein-peptide interactions. Bioinform. 22 (5) (2006)
Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams
Prediction of Protein-Peptide Interactions. Systems Biology and Regulatory Genomics (2005)
Dirk Husmeier
Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models. ECCB/JBI (2005)
Dirk Husmeier, Frank Wright, Iain Milne
Detecting interspecific recombination with a pruned probabilistic divergence measure. Bioinform. 21 (9) (2005)
Iain Milne, Frank Wright, Glenn Rowe, David F. Marshall, Dirk Husmeier, Gráinne McGuire
TOPALi: software for automatic identification of recombinant sequences within DNA multiple alignments. Bioinform. 20 (11) (2004)
Dirk Husmeier
Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinform. 19 (17) (2003)
Dirk Husmeier, Frank Wright
Detection of Recombination in DNA Multiple Alignments with Hidden Markov Models. J. Comput. Biol. 8 (4) (2002)
Dirk Husmeier, Gráinne McGuire
Detecting recombination with MCMC. ISMB (2002)
Dirk Husmeier, Frank Wright
A Bayesian approach to discriminate between alternative DNA sequence segmentations. Bioinform. 18 (2) (2002)
Kaspar Althoefer, Bart Krekelberg, Dirk Husmeier, Lakmal D. Seneviratne
Reinforcement learning in a rule-based navigator for robotic manipulators. Neurocomputing 37 (1-4) (2001)
Dirk Husmeier, Frank Wright
Probabilistic divergence measures for detecting interspecies recombination. ISMB (Supplement of Bioinformatics) (2001)
Dirk Husmeier, Frank Wright
Approximate Bayesian Discrimination between Alternative DNA Mosaic Structures. German Conference on Bioinformatics (2001)
Dirk Husmeier
Learning non-stationary conditional probability distributions. Neural Networks 13 (3) (2000)
Dirk Husmeier
The Bayesian Evidence Scheme for Regularizing Probability-Density Estimating Neural Networks. Neural Comput. 12 (11) (2000)
Dirk Husmeier, Frank Wright
Detecting Sporadic Recombination in DNA Alignments with Hidden Markov Models. German Conference on Bioinformatics (2000)
Dirk Husmeier, Gillian S. Patton, Myra O. McClure, John R. W. Harris, Stephen J. Roberts
Neural networks for predicting Kaposi's sarcoma. IJCNN (1999)
Dirk Husmeier, William D. Penny, Stephen J. Roberts
An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers. Neural Networks 12 (4-5) (1999)
Stephen J. Roberts, Dirk Husmeier, Iead Rezek, William D. Penny
Bayesian Approaches to Gaussian Mixture Modeling. IEEE Trans. Pattern Anal. Mach. Intell. 20 (11) (1998)
Dirk Husmeier, John G. Taylor
Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL. Neural Networks 11 (1) (1998)
Dirk Husmeier, John G. Taylor
Modeling Conditional Probabilities with Committees of RVFL Networks. ICANN (1997)
Dirk Husmeier, John G. Taylor
Predicting Conditional Probability Densities of Stationary Stochastic Time Series. Neural Networks 10 (3) (1997)
Dirk Husmeier, John G. Taylor
Predicting Conditional Probability Densities with the Gaussian Mixture - RVFL Network. ICANNGA (1997)