Dirk Husmeier - RankMe

82 papers A* 4A 7B 1C 1Journal 50Unranked 16

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Advanced statistical inference of myocardial stiffness: A time series Gaussian process approach of emulating cardiac mechanics for real-time clinical decision support. Comput. Biol. Medicine Yuzhang Ge, Dirk Husmeier, Arash Rabbani, Hao Gao
2025	J	`jnl`	Approximate Bayesian inference in a model for self-generated gradient collective cell movement. Comput. Stat. Jon Devlin, Agnieszka Borowska, Dirk Husmeier, John A. Mackenzie
2024	J	`jnl`	Automatic detection of myocardial ischaemia using generalisable spatio-temporal hierarchical Bayesian modelling of DCE-MRI. Comput. Medical Imaging Graph. Yalei Yang, Dirk Husmeier, Hao Gao, Colin Berry, David Carrick, Aleksandra Radjenovic
2024	J	`jnl`	Bayesian Inversion of Frequency-Domain Airborne EM Data With Spatial Correlation Prior Information. IEEE Trans. Geosci. Remote. Sens. Jianmei Zhou, Dirk Husmeier, Hao Gao, Changchun Yin, Changkai Qiu, Xu Jing, Yanfu Qi, Wentao Liu
2024	J	`jnl`	Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations. J. Mach. Learn. Res. David Dalton, Alan Lazarus, Hao Gao, Dirk Husmeier
2024	A*	`conf`	Physics and Lie symmetry informed Gaussian processes. ICML David Dalton, Dirk Husmeier, Hao Gao
2023	J	`jnl`	A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. PLoS Comput. Biol. William T. Harvey, Vinny Davies, Rodney S. Daniels, Lynne Whittaker, Victoria Gregory, Alan J. Hay, Dirk Husmeier, John W. McCauley, Richard E. Reeve
2023	J	`jnl`	Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications. Comput. Medical Imaging Graph. Arash Rabbani, Hao Gao, Alan Lazarus, David Dalton, Yuzhang Ge, Kenneth Mangion, Colin Berry, Dirk Husmeier
2023	J	`jnl`	Stochastic variational inference for scalable non-stationary Gaussian process regression. Stat. Comput. Ionut Paun, Dirk Husmeier, Colin J. Torney
2023	A	`conf`	The Fully Convolutional Transformer for Medical Image Segmentation. WACV Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith, Dirk Husmeier
2022	J	`jnl`	Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models. Stat. Comput. Luiza Mihaela Paun, Dirk Husmeier
2022	J	`jnl`	Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking. CoRR Arash Rabbani, Hao Gao, Dirk Husmeier
2022	J	`jnl`	The Fully Convolutional Transformer for Medical Image Segmentation. CoRR Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith, Dirk Husmeier
2022	J	`jnl`	Transferable species distribution modelling: Comparative performance of Generalised Functional Response models. Ecol. Informatics Shaykhah Aldossari, Dirk Husmeier, Jason Matthiopoulos
2021	J	`jnl`	Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis. J. Comput. Phys. Agnieszka Borowska, Diana Giurghita, Dirk Husmeier
2021	J	`jnl`	Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. Artif. Intell. Medicine Lukasz Romaszko, Agnieszka Borowska, Alan Lazarus, David Dalton, Colin Berry, Xiaoyu Luo, Dirk Husmeier, Hao Gao
2021	J	`jnl`	R package for statistical inference in dynamical systems using kernel based gradient matching: KGode. Comput. Stat. Mu Niu, Joe Wandy, Rónán Daly, Simon Rogers, Dirk Husmeier
2019	J	`jnl`	Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching. Stat. Comput. Benn Macdonald, Dirk Husmeier
2018	A	`conf`	Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations. AISTATS Alan Lazarus, Dirk Husmeier, Theodore Papamarkou
2018	J	`jnl`	On a New Improvement-Based Acquisition Function for Bayesian Optimization. CoRR Umberto Noè, Dirk Husmeier
2018	J	`jnl`	ShinyKGode: an interactive application for ODE parameter inference using gradient matching. Bioinform. Joe Wandy, Mu Niu, Diana Giurghita, Rónán Daly, Simon Rogers, Dirk Husmeier
2018	J	`jnl`	Statistical inference in mechanistic models: time warping for improved gradient matching. Comput. Stat. Mu Niu, Benn Macdonald, Simon Rogers, Maurizio Filippone, Dirk Husmeier
2017	J	`jnl`	A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution. Comput. Stat. Vinny Davies, Richard E. Reeve, William T. Harvey, Francois F. Maree, Dirk Husmeier
2017	J	`jnl`	Approximate Bayesian inference in semi-mechanistic models. Stat. Comput. Andrej Aderhold, Dirk Husmeier, Marco Grzegorczyk
2017	J	`jnl`	Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration. Comput. Stat. Marco Grzegorczyk, Andrej Aderhold, Dirk Husmeier
2016	A*	`conf`	Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching. ICML Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier
2016	—	`conf`	Inference in a Partial Differential Equations Model of Pulmonary Arterial and Venous Blood Circulation Using Statistical Emulation. CIBB Umberto Noè, Weiwei Chen, Maurizio Filippone, Nicholas Hill, Dirk Husmeier
2016	—	`conf`	Parameter Inference in Differential Equation Models of Biopathways Using Time Warped Gradient Matching. CIBB Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier
2015	—	`conf`	Computational Inference in Systems Biology. IWBBIO (2) Benn Macdonald, Dirk Husmeier
2015	A*	`conf`	Controversy in mechanistic modelling with Gaussian processes. ICML Benn Macdonald, Catherine F. Higham, Dirk Husmeier
2015	—	`conf`	Inference of Circadian Regulatory Pathways Based on Delay Differential Equations. IWBBIO (2) Catherine F. Higham, Dirk Husmeier
2015	—	`conf`	Selecting Random Effect Components in a Sparse Hierarchical Bayesian Model for Identifying Antigenic Variability. CIBB Vinny Davies, Richard E. Reeve, William T. Harvey, Dirk Husmeier
2014	—	`conf`	Inference of Circadian Regulatory Networks. IWBBIO Marco Grzegorczyk, Andrej Aderhold, V. Anne Smith, Dirk Husmeier
2014	A	`conf`	Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus. AISTATS Vinny Davies, Richard E. Reeve, William T. Harvey, Francois F. Maree, Dirk Husmeier
2013	J	`jnl`	A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana. BMC Bioinform. Catherine F. Higham, Dirk Husmeier
2013	J	`jnl`	Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Mach. Learn. Frank Dondelinger, Sophie Lèbre, Dirk Husmeier
2013	A	`conf`	ODE parameter inference using adaptive gradient matching with Gaussian processes. AISTATS Frank Dondelinger, Dirk Husmeier, Simon Rogers, Maurizio Filippone
2013	A	`conf`	Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes. AISTATS Andrej Aderhold, Dirk Husmeier, V. Anne Smith
2013	J	`jnl`	Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models. Mach. Learn. Marco Grzegorczyk, Dirk Husmeier
2012	A	`conf`	Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters. AISTATS Marco Grzegorczyk, Dirk Husmeier
2012	J	`jnl`	Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data. Ecol. Informatics Andrej Aderhold, Dirk Husmeier, Jack J. Lennon, Colin M. Beale, V. Anne Smith
2011	J	`jnl`	Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes. Bioinform. Marco Grzegorczyk, Dirk Husmeier
2011	J	`jnl`	Modelling non-stationary dynamic gene regulatory processes with the BGM model. Comput. Stat. Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer
2011	J	`jnl`	Non-homogeneous dynamic Bayesian networks for continuous data. Mach. Learn. Marco Grzegorczyk, Dirk Husmeier
2010	A*	`conf`	Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing. ICML Frank Dondelinger, Sophie Lèbre, Dirk Husmeier
2010	J	`jnl`	Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods. Ecol. Informatics Ali Faisal, Frank Dondelinger, Dirk Husmeier, Colin M. Beale
2010	—	`conf`	Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks. NIPS Dirk Husmeier, Frank Dondelinger, Sophie Lèbre
2010	—	`ch.`	Mixtures of Factor Analyzers for Modeling Transcriptional Regulation. Learning and Inference in Computational Systems Biology Kuang Lin, Dirk Husmeier
2010	J	`jnl`	Modelling Nonstationary Gene Regulatory Processes. Adv. Bioinformatics Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer
2009	—	`conf`	Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks. PRIB Marco Grzegorczyk, Dirk Husmeier
2009	—	`conf`	Distinguishing Regional from Within-Codon Rate Heterogeneity in DNA Sequence Alignments. PRIB Alexander V. Mantzaris, Dirk Husmeier
2009	J	`jnl`	Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization. EURASIP J. Bioinform. Syst. Biol. Kuang Lin, Dirk Husmeier
2009	—	`conf`	Non-stationary continuous dynamic Bayesian networks. NIPS Marco Grzegorczyk, Dirk Husmeier
2009	J	`jnl`	TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops. Bioinform. Iain Milne, Dominik Lindner, Micha Bayer, Dirk Husmeier, Gráinne McGuire, David F. Marshall, Frank Wright
2008	J	`jnl`	Gene Regulatory Network Reconstruction by Bayesian Integration of Prior Knowledge and/or Different Experimental Conditions. J. Bioinform. Comput. Biol. Adriano Velasque Werhli, Dirk Husmeier
2008	J	`jnl`	Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Mach. Learn. Marco Grzegorczyk, Dirk Husmeier
2008	J	`jnl`	Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler. Bioinform. Marco Grzegorczyk, Dirk Husmeier, Kieron D. Edwards, Peter Ghazal, Andrew J. Millar
2006	J	`jnl`	A regularized discriminative model for the prediction of protein-peptide interactions. Bioinform. Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams
2006	J	`jnl`	Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinform. Adriano Velasque Werhli, Marco Grzegorczyk, Dirk Husmeier
2005	J	`jnl`	Detecting interspecific recombination with a pruned probabilistic divergence measure. Bioinform. Dirk Husmeier, Frank Wright, Iain Milne
2005	—	`conf`	Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models. ECCB/JBI Dirk Husmeier
2005	—	`conf`	Probabilistic Systems Biology and Regulatory Genomics Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams
2004	J	`jnl`	TOPALi: software for automatic identification of recombinant sequences within DNA multiple alignments. Bioinform. Iain Milne, Frank Wright, Glenn Rowe, David F. Marshall, Dirk Husmeier, Gráinne McGuire
2003	J	`jnl`	Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinform. Dirk Husmeier
2002	J	`jnl`	A Bayesian approach to discriminate between alternative DNA sequence segmentations. Bioinform. Dirk Husmeier, Frank Wright
2002	A	`conf`	Detecting recombination with MCMC. ISMB Dirk Husmeier, Gráinne McGuire
2002	J	`jnl`	Detection of Recombination in DNA Multiple Alignments with Hidden Markov Models. J. Comput. Biol. Dirk Husmeier, Frank Wright
2001	—	`conf`	Approximate Bayesian Discrimination between Alternative DNA Mosaic Structures. German Conference on Bioinformatics Dirk Husmeier, Frank Wright
2001	—	`conf`	Probabilistic divergence measures for detecting interspecies recombination. ISMB (Supplement of Bioinformatics) Dirk Husmeier, Frank Wright
2001	J	`jnl`	Reinforcement learning in a rule-based navigator for robotic manipulators. Neurocomputing Kaspar Althoefer, Bart Krekelberg, Dirk Husmeier, Lakmal D. Seneviratne
2000	—	`conf`	Detecting Sporadic Recombination in DNA Alignments with Hidden Markov Models. German Conference on Bioinformatics Dirk Husmeier, Frank Wright
2000	J	`jnl`	Learning non-stationary conditional probability distributions. Neural Networks Dirk Husmeier
2000	J	`jnl`	The Bayesian Evidence Scheme for Regularizing Probability-Density Estimating Neural Networks. Neural Comput. Dirk Husmeier
2000	—	`ch.`	The Bayesian Paradigm: Second Generation Neural Computing. Artificial Neural Networks in Biomedicine William D. Penny, Dirk Husmeier, Stephen J. Roberts
1999	J	`jnl`	An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers. Neural Networks Dirk Husmeier, William D. Penny, Stephen J. Roberts
1999	—	`book`	Neural networks for conditional probability estimation - forecasting beyond point predictions. Dirk Husmeier
1999	B	`conf`	Neural networks for predicting Kaposi's sarcoma. IJCNN Dirk Husmeier, Gillian S. Patton, Myra O. McClure, John R. W. Harris, Stephen J. Roberts
1998	J	`jnl`	Bayesian Approaches to Gaussian Mixture Modeling. IEEE Trans. Pattern Anal. Mach. Intell. Stephen J. Roberts, Dirk Husmeier, Iead Rezek, William D. Penny
1998	J	`jnl`	Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL. Neural Networks Dirk Husmeier, John G. Taylor
1997	C	`conf`	Modeling Conditional Probabilities with Committees of RVFL Networks. ICANN Dirk Husmeier, John G. Taylor
1997	J	`jnl`	Predicting Conditional Probability Densities of Stationary Stochastic Time Series. Neural Networks Dirk Husmeier, John G. Taylor
1997	—	`conf`	Predicting Conditional Probability Densities with the Gaussian Mixture - RVFL Network. ICANNGA Dirk Husmeier, John G. Taylor