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ORCIDDirk Husmeier
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2020 – today
- 2024
[j44]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: 102333 (2024)- [j43]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: 1-16 (2024) - [c29]David Dalton, Dirk Husmeier, Hao Gao:
Physics and Lie symmetry informed Gaussian processes. ICML 2024 - 2023
- [j42]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: 102203 (2023) - [j41]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) - [j40]Ionut Paun, Dirk Husmeier, Colin J. Torney:
Stochastic variational inference for scalable non-stationary Gaussian process regression. Stat. Comput. 33(2): 44 (2023) - [c28]Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith, Dirk Husmeier:
The Fully Convolutional Transformer for Medical Image Segmentation. WACV 2023: 3649-3658 - 2022
- [j39]Shaykhah Aldossari, Dirk Husmeier, Jason Matthiopoulos:
Transferable species distribution modelling: Comparative performance of Generalised Functional Response models. Ecol. Informatics 71: 101803 (2022) - [j38]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): 1 (2022) - [i3]Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith, Dirk Husmeier:
The Fully Convolutional Transformer for Medical Image Segmentation. CoRR abs/2206.00566 (2022) - [i2]Arash Rabbani, Hao Gao, Dirk Husmeier:
Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking. CoRR abs/2208.00165 (2022) - 2021
- [j37]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: 102140 (2021) - [j36]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): 715-747 (2021) - [j35]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: 109999 (2021)
2010 – 2019
- 2019
- [j34]Benn Macdonald, Dirk Husmeier:
Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching. Stat. Comput. 29(5): 853-867 (2019) - 2018
- [j33]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): 2314-2315 (2018) - [j32]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): 1091-1123 (2018) - [c27]Alan Lazarus, Dirk Husmeier, Theodore Papamarkou:
Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations. AISTATS 2018: 1252-1260 - [i1]Umberto Noè, Dirk Husmeier:
On a New Improvement-Based Acquisition Function for Bayesian Optimization. CoRR abs/1808.06918 (2018) - 2017
- [j31]Marco Grzegorczyk, Andrej Aderhold, Dirk Husmeier:
Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration. Comput. Stat. 32(2): 717-761 (2017) - [j30]Vinny Davies, Richard E. 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): 803-843 (2017) - [j29]Andrej Aderhold, Dirk Husmeier, Marco Grzegorczyk:
Approximate Bayesian inference in semi-mechanistic models. Stat. Comput. 27(4): 1003-1040 (2017) - 2016
- [c26]Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier:
Parameter Inference in Differential Equation Models of Biopathways Using Time Warped Gradient Matching. CIBB 2016: 145-159 - [c25]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: 184-198 - [c24]Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier:
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching. ICML 2016: 1699-1707 - 2015
- [c23]Vinny Davies, Richard E. Reeve, William T. Harvey, Dirk Husmeier:
Selecting Random Effect Components in a Sparse Hierarchical Bayesian Model for Identifying Antigenic Variability. CIBB 2015: 14-27 - [c22]Benn Macdonald, Catherine F. Higham, Dirk Husmeier:
Controversy in mechanistic modelling with Gaussian processes. ICML 2015: 1539-1547 - [c21]Benn Macdonald, Dirk Husmeier:
Computational Inference in Systems Biology. IWBBIO (2) 2015: 276-288 - [c20]Catherine F. Higham, Dirk Husmeier:
Inference of Circadian Regulatory Pathways Based on Delay Differential Equations. IWBBIO (2) 2015: 468-478 - 2014
- [c19]Vinny Davies, Richard E. 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: 149-158 - [c18]Marco Grzegorczyk, Andrej Aderhold, V. Anne Smith, Dirk Husmeier:
Inference of Circadian Regulatory Networks. IWBBIO 2014: 1001-1014 - 2013
- [j28]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): S3 (2013) - [j27]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): 191-230 (2013) - [j26]Marco Grzegorczyk, Dirk Husmeier:
Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models. Mach. Learn. 91(1): 105-154 (2013) - [c17]Andrej Aderhold, Dirk Husmeier, V. Anne Smith:
Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes. AISTATS 2013: 75-84 - [c16]Frank Dondelinger, Dirk Husmeier, Simon Rogers, Maurizio Filippone:
ODE parameter inference using adaptive gradient matching with Gaussian processes. AISTATS 2013: 216-228 - 2012
- [j25]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: 55-64 (2012) - [c15]Marco Grzegorczyk, Dirk Husmeier:
Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters. AISTATS 2012: 467-476 - 2011
- [j24]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): 693-699 (2011) - [j23]Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer:
Modelling non-stationary dynamic gene regulatory processes with the BGM model. Comput. Stat. 26(2): 199-218 (2011) - [j22]Marco Grzegorczyk, Dirk Husmeier:
Non-homogeneous dynamic Bayesian networks for continuous data. Mach. Learn. 83(3): 355-419 (2011) - 2010
- [j21]Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer:
Modelling Nonstationary Gene Regulatory Processes. Adv. Bioinformatics 2010: 749848:1-749848:17 (2010) - [j20]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): 451-464 (2010) - [c14]Frank Dondelinger, Sophie Lèbre, Dirk Husmeier:
Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing. ICML 2010: 303-310 - [c13]Dirk Husmeier, Frank Dondelinger, Sophie Lèbre:
Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks. NIPS 2010: 901-909 - [p2]Kuang Lin, Dirk Husmeier:
Mixtures of Factor Analyzers for Modeling Transcriptional Regulation. Learning and Inference in Computational Systems Biology 2010: 153-200
2000 – 2009
- 2009
- [j19]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): 126-127 (2009) - [j18]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) - [c12]Marco Grzegorczyk, Dirk Husmeier:
Non-stationary continuous dynamic Bayesian networks. NIPS 2009: 682-690 - [c11]Marco Grzegorczyk, Dirk Husmeier:
Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks. PRIB 2009: 113-124 - [c10]Alexander V. Mantzaris, Dirk Husmeier:
Distinguishing Regional from Within-Codon Rate Heterogeneity in DNA Sequence Alignments. PRIB 2009: 187-198 - 2008
- [j17]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): 2071-2078 (2008) - [j16]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): 543-572 (2008) - [j15]Marco Grzegorczyk, Dirk Husmeier:
Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Mach. Learn. 71(2-3): 265-305 (2008) - 2006
- [j14]Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams:
A regularized discriminative model for the prediction of protein-peptide interactions. Bioinform. 22(5): 532-540 (2006) - [j13]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): 2523-2531 (2006) - 2005
- [j12]Dirk Husmeier, Frank Wright, Iain Milne:
Detecting interspecific recombination with a pruned probabilistic divergence measure. Bioinform. 21(9): 1797-1806 (2005) - [c9]Dirk Husmeier:
Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models. ECCB/JBI 2005: 172 - [c8]Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams:
Probabilistic in Silico Prediction of Protein-Peptide Interactions. Systems Biology and Regulatory Genomics 2005: 188-197 - 2004
- [j11]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): 1806-1807 (2004) - 2003
- [j10]Dirk Husmeier:
Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinform. 19(17): 2271-2282 (2003) - 2002
- [j9]Dirk Husmeier, Frank Wright:
A Bayesian approach to discriminate between alternative DNA sequence segmentations. Bioinform. 18(2): 226-234 (2002) - [j8]Dirk Husmeier, Frank Wright:
Detection of Recombination in DNA Multiple Alignments with Hidden Markov Models. J. Comput. Biol. 8(4): 401-427 (2002) - [c7]Dirk Husmeier, Gráinne McGuire:
Detecting recombination with MCMC. ISMB 2002: 345-353 - 2001
- [j7]Kaspar Althoefer, Bart Krekelberg, Dirk Husmeier, Lakmal D. Seneviratne:
Reinforcement learning in a rule-based navigator for robotic manipulators. Neurocomputing 37(1-4): 51-70 (2001) - [c6]Dirk Husmeier, Frank Wright:
Approximate Bayesian Discrimination between Alternative DNA Mosaic Structures. German Conference on Bioinformatics 2001: 182-184 - [c5]Dirk Husmeier, Frank Wright:
Probabilistic divergence measures for detecting interspecies recombination. ISMB (Supplement of Bioinformatics) 2001: 123-131 - 2000
- [j6]Dirk Husmeier:
The Bayesian Evidence Scheme for Regularizing Probability-Density Estimating Neural Networks. Neural Comput. 12(11): 2685-2717 (2000) - [j5]Dirk Husmeier:
Learning non-stationary conditional probability distributions. Neural Networks 13(3): 287-290 (2000) - [c4]Dirk Husmeier, Frank Wright:
Detecting Sporadic Recombination in DNA Alignments with Hidden Markov Models. German Conference on Bioinformatics 2000: 19-26 - [p1]William D. Penny, Dirk Husmeier, Stephen J. Roberts:
The Bayesian Paradigm: Second Generation Neural Computing. Artificial Neural Networks in Biomedicine 2000: 11-23
1990 – 1999
- 1999
- [b1]Dirk Husmeier:
Neural networks for conditional probability estimation - forecasting beyond point predictions. Perspectives in neural computing, Springer 1999, ISBN 978-1-85233-095-8, pp. I-XXIII, 1-275 - [j4]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): 677-705 (1999) - [c3]Dirk Husmeier, Gillian S. Patton, Myra O. McClure, John R. W. Harris, Stephen J. Roberts:
Neural networks for predicting Kaposi's sarcoma. IJCNN 1999: 3707-3711 - 1998
- [j3]Dirk Husmeier, John G. Taylor:
Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL. Neural Networks 11(1): 89-116 (1998) - [j2]Stephen J. Roberts, Dirk Husmeier, Iead Rezek, William D. Penny:
Bayesian Approaches to Gaussian Mixture Modeling. IEEE Trans. Pattern Anal. Mach. Intell. 20(11): 1133-1142 (1998) - 1997
- [j1]Dirk Husmeier, John G. Taylor:
Predicting Conditional Probability Densities of Stationary Stochastic Time Series. Neural Networks 10(3): 479-497 (1997) - [c2]Dirk Husmeier, John G. Taylor:
Modeling Conditional Probabilities with Committees of RVFL Networks. ICANN 1997: 1053-1058 - [c1]Dirk Husmeier, John G. Taylor:
Predicting Conditional Probability Densities with the Gaussian Mixture - RVFL Network. ICANNGA 1997: 477-481
Coauthor Index
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last updated on 2024-10-07 21:20 CEST by the dblp team
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