Dirk Husmeier

Stephen J. Roberts, Dirk Husmeier, Gillian S. Patton, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

A Bayesian approach to discriminate between alternative DNA sequence segmentations

Dirk Husmeier, Frank Wright, 2002, Bioinform..

Reverse Engineering Gene Regulatory Networks with Various Machine Learning Methods

Adriano V. Werhli, Marco Grzegorczyk, Dirk Husmeier, 2008 .

Parameter Inference in Differential Equation Models of Biopathways Using Time Warped Gradient Matching

Dirk Husmeier, Maurizio Filippone, Simon Rogers, 2016, CIBB.

Neural networks for conditional probability estimation - forecasting beyond point predictions

Dirk Husmeier, D. Husmeier, 1999, Perspectives in neural computing.

Modelling Nonstationary Gene Regulatory Processes

Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer, 2010, Adv. Bioinformatics.

Statistical Modelling of Cell Movement Data Using the Unscented Kalman Filter

Dirk Husmeier, Diana Giurghita, 2017 .

Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2012, AISTATS.

Predicting Conditional Probability Densities of Stationary Stochastic Time Series

Dirk Husmeier, John G. Taylor, John G. Taylor, 1997, Neural Networks.

Approximate Bayesian Discrimination between Alternative DNA Mosaic Structures

Dirk Husmeier, Frank Wright, 2001, German Conference on Bioinformatics.

TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops

Dirk Husmeier, Iain Milne, David F. Marshall, 2008, Bioinform..

A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana

Dirk Husmeier, Catherine F. Higham, D. Husmeier, 2013, BMC Bioinformatics.

A neural network approach to predicting noisy time series

Dirk Husmeier, John G. Taylor, John G. Taylor, 1996 .

TOPALi: software for automatic identification of recombinant sequences within DNA multiple alignments

Dirk Husmeier, Iain Milne, David F. Marshall, 2004, Bioinform..

Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes

Dirk Husmeier, V. Anne Smith, Andrej Aderhold, 2013, AISTATS.

Probabilistic divergence measures for detecting interspecies recombination

Dirk Husmeier, Frank Wright, D. Husmeier, 2001, ISMB.

Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks

Dirk Husmeier, D. Husmeier, 2003, Bioinform..

Modelling transcriptional regulation with Gaussian processes

Dirk Husmeier, Vinny Davies, 2014 .

Computational inference of biopathway dynamics and structures

Dirk Husmeier, Maurizio Filippone, Simon Rogers, 2017 .

Inferring Genetic Regulatory Networks from Microarray Experiments with Bayesian Networks

Dirk Husmeier, 2005 .

On a New Improvement-Based Acquisition Function for Bayesian Optimization pdf

Dirk Husmeier, Umberto Noè, D. Husmeier, 2018, ArXiv.

Statistical Emulation of Cardiac Mechanics: an Important Step Towards a Clinical Decision Support System

Dirk Husmeier, Alan Lazarus, Umberto Noè, 2019 .

Supplementary material from "Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries"

Dirk Husmeier, Naomi C. Chesler, L. Mihaela Paun, 2019 .

Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries

Dirk Husmeier, L. Mihaela Paun, Mitchel J. Colebank, 2019, Journal of the Royal Society Interface.

Fast inference in nonlinear dynamical systems using gradient matching

Dirk Husmeier, Maurizio Filippone, Simon Rogers, 2016, ICML 2016.

The Bayesian Evidence Scheme for Regularizing Probability-Density Estimating Neural Networks

Dirk Husmeier, D. Husmeier, 2000, Neural Computation.

Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2008, Machine Learning.

Quantification of Myocardial Perfusion Lesions Using Spatially Variant Finite Mixture Modelling of DCE-MRI

Dirk Husmeier, Yalei Yang, Hao Gao, 2019 .

A Bayesian regression and multiple changepoint model for systems biology

Marco Grzegorczyk, Dirk Husmeier, Sophie Lèbre, 2011 .

Modelling non-stationary gene regulatory processes with a non-homogeneous dynamic Bayesian network and the change point process

Marco Grzegorczyk, Dirk Husmeier, 2009 .

Probabilistic in Silico Prediction of Protein-Peptide Interactions

Dirk Husmeier, Christopher K. I. Williams, Wolfgang P. Lehrach, 2005, Systems Biology and Regulatory Genomics.

Closed-loop effects in cardiovascular clinical decision support

Dirk Husmeier, L. Mihaela Paun, 2020 .

Network Reconstruction with Realistic Models

Marco Grzegorczyk, Dirk Husmeier, Andrej Aderhold, 2015 .

Approximate Bayesian inference in semi-mechanistic models

Marco Grzegorczyk, Dirk Husmeier, Andrej Aderhold, 2016, Statistics and Computing.

A Real-World Application: The Boston Housing Data

Dirk Husmeier, 1999 .

A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution

Dirk Husmeier, Vinny Davies, Richard Reeve, 2017, Comput. Stat..

Controversy in mechanistic modelling with Gaussian processes

Dirk Husmeier, Benn Macdonald, Catherine F. Higham, 2015, ICML.

The Bayesian Evidence Scheme for Model Selection

Dirk Husmeier, 1999 .

An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers

William D. Penny, Stephen J. Roberts, Dirk Husmeier, 1999, Neural Networks.

Modelling multiscale collective behavior with Gaussian processes

Dirk Husmeier, Colin J. Torney, Juan M. Morales, 2020 .

Probabilistic Modeling in Bioinformatics and Medical Informatics

Stephen J. Roberts, Dirk Husmeier, Richard Dybowski, 2010 .

Statistical Inference of the Drivers of Collective Cell Movement

Dirk Husmeier, Jason Matthiopoulos, Robert H. Insall, 2017 .

Statistical inference in mechanistic models: time warping for improved gradient matching

Dirk Husmeier, Maurizio Filippone, Simon Rogers, 2018, Comput. Stat..

Gradient Matching Methods for Computational Inference in Mechanistic Models for Systems Biology: A Review and Comparative Analysis

Dirk Husmeier, Benn Macdonald, D. Husmeier, 2015, Front. Bioeng. Biotechnol..

Reverse engineering gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum.

Hui Liu, Dirk Husmeier, Frank Dondelinger, 2010, Methods in molecular biology.

Modelling non-stationary dynamic gene regulatory processes with the BGM model

Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer, 2011, Comput. Stat..

Massive Dimensionality Reduction for the Left Ventricular Mesh

Dirk Husmeier, Alan Lazarus, Lukasz Romaszko, 2019, Proceedings of the International Conference on Statistics: Theory and Applications.

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks

Marco Grzegorczyk, Dirk Husmeier, Adriano Velasque Werhli, 2006, Bioinform..

Computationally efficient parameter estimation and uncertainty quantification in complex physiological systems

Dirk Husmeier, D. Husmeier, 2020 .

Detecting interspecific recombination with a pruned probabilistic divergence measure

Dirk Husmeier, Iain Milne, Frank Wright, 2005, Bioinform..

Modeling Conditional Probabilities with Committees of RVFL Networks

Dirk Husmeier, John G. Taylor, John G. Taylor, 1997, ICANN.

Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure

Dirk Husmeier, Frank Dondelinger, Sophie Lèbre, 2012, Machine Learning.

Improved Training Scheme Combining the Expectation Maximisation (EM) Algorithm with the RVFL Approach

Dirk Husmeier, 1999 .

Demonstration: Committees of Networks Trained with Different Regularisation Schemes

Dirk Husmeier, 1999 .

MCMC methods for inference in a mathematical model of pulmonary circulation

Dirk Husmeier, L. Mihaela Paun, M. Umar Qureshi, 2018, 1801.07742.

Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching

Dirk Husmeier, Maurizio Filippone, Simon Rogers, 2016, ICML.

ODE parameter inference using adaptive gradient matching with Gaussian processes

Dirk Husmeier, Maurizio Filippone, Simon Rogers, 2013, AISTATS.

Approximate Bayesian Inference for Individual-based Models with Emergent Dynamics

Dirk Husmeier, Colin J. Torney, Juan M. Morales, 2020, Proceedings of the 2nd International Conference on Statistics: Theory and Applications.

Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL

Dirk Husmeier, John G. Taylor, John G. Taylor, 1998, Neural Networks.

Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching

Dirk Husmeier, Benn Macdonald, D. Husmeier, 2018, Stat. Comput..

Parameter Inference in the Pulmonary Circulation of Mice

Dirk Husmeier, L. Mihaela Paun, M. Umar Qureshi, 2017 .

A Study on Discrete-Time Movement Models

Dirk Husmeier, Colin J. Torney, Ionut Alexandru Paun, 2019 .

Non-stationary continuous dynamic Bayesian networks

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2009, NIPS.

Automatic Relevance Determination (ARD)

Dirk Husmeier, 1999 .

Empirical Demonstration: Combining EM and RVFL

Dirk Husmeier, 1999 .

Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation

Colin Berry, Hao Gao, Dirk Husmeier, 2019, Journal of the Royal Statistical Society. Series C, Applied statistics.

Introduction to Learning Bayesian Networks from Data

Dirk Husmeier, D. Husmeier, 2005 .

Hierarchical Bayesian Regression (HBR) and Analytic Gradient Calculation (GCGP)

Marco Grzegorczyk, Dirk Husmeier, Andrej Aderhold, 2016 .

Covariance-based weighting for optimal combination of model predictions

Stephen J. Roberts, Dirk Husmeier, William D. Penny, 1999 .

R package for statistical inference in dynamical systems using kernel based gradient matching: KGode

Dirk Husmeier, Simon Rogers, Joe Wandy, 2020, Computational Statistics.

Erratum: Computational Inference in Systems Biology

Dirk Husmeier, Benn Macdonald, 2015 .

Learning non-stationary conditional probability distributions

Dirk Husmeier, 2000, Neural Networks.

Gene Regulatory Network Reconstruction by Bayesian Integration of Prior Knowledge and/or Different Experimental Conditions

Dirk Husmeier, Adriano Velasque Werhli, D. Husmeier, 2008, J. Bioinform. Comput. Biol..

Predicting Conditional Probability Densities with the Gaussian Mixture - RVFL Network

Dirk Husmeier, John G. Taylor, John G. Taylor, 1997, ICANNGA.

Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks

Marco Grzegorczyk, Dirk Husmeier, 2009, PRIB.

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

Dirk Husmeier, Ali Faisal, Frank Dondelinger, 2010, Ecol. Informatics.

Detection of Recombination in DNA Multiple Alignments with Hidden Markov Models

Dirk Husmeier, Frank Wright, D. Husmeier, 2002, J. Comput. Biol..

Estimating Parameters of Partial Differential Equations with Gradient Matching

Dirk Husmeier, Benn Macdonald, Zhongyi Liu, 2017 .

Reinforcement learning in a rule-based navigator for robotic manipulators

Kaspar Althoefer, Dirk Husmeier, Lakmal D. Seneviratne, 2001, Neurocomputing.

Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization

Kuang Lin, Dirk Husmeier, D. Husmeier, 2009, EURASIP J. Bioinform. Syst. Biol..

Modelling conditional probabilities with network committees: how overfitting can be useful

Kaspar Althoefer, Dirk Husmeier, 1998 .

Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2011, Bioinform..

ShinyKGode: an interactive application for ODE parameter inference using gradient matching

Dirk Husmeier, Joe Wandy, Simon Rogers, 2018, Bioinform..

Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis

Dirk Husmeier, Agnieszka Borowska, Diana Giurghita, 2020, J. Comput. Phys..

Mixtures of Factor Analyzers for Modeling Transcriptional Regulation

Dirk Husmeier, K. Lin, 2010, Learning and Inference in Computational Systems Biology.

Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance

Hao Gao, Dirk Husmeier, Vinny Davies, 2019, Journal of the Royal Society Interface.

Demonstration of the Bayesian Evidence Scheme for Regularisation

Dirk Husmeier, 1999 .

Computational Inference in Systems Biology

Dirk Husmeier, Benn Macdonald, D. Husmeier, 2015, IWBBIO.

Detecting Sporadic Recombination in DNA Alignments with Hidden Markov Models

Dirk Husmeier, Frank Wright, 2000, German Conference on Bioinformatics.

Closed-loop effects in coupling cardiac physiological models to clinical interventions

Dirk Husmeier, L. Mihaela Paun, 2020 .

The Bayesian Paradigm: Second Generation Neural Computing

Stephen J. Roberts, Dirk Husmeier, William D. Penny, 2000, Artificial Neural Networks in Biomedicine.

Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations

Dirk Husmeier, Theodore Papamarkou, Alan Lazarus, 2018, AISTATS.

Supplementary material from "Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance"

Hao Gao, Dirk Husmeier, Vinny Davies, 2019 .

Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing

Dirk Husmeier, Frank Dondelinger, Sophie Lèbre, 2010, ICML.

Inference on the Duffing System With the Unscented Kalman Filter and Optimization of Sigma Points

Dirk Husmeier, Alessandra Luati, Umberto Noè, 2017 .

Demonstration of the Model Performance on the Benchmark Problems

Dirk Husmeier, 1999 .

Inference in a Partial Differential Equations Model of Pulmonary Arterial and Venous Blood Circulation Using Statistical Emulation

Dirk Husmeier, Maurizio Filippone, Weiwei Chen, 2016, CIBB.

Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus

Dirk Husmeier, Vinny Davies, Richard Reeve, 2014, AISTATS.

Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler

Marco Grzegorczyk, Dirk Husmeier, Andrew J. Millar, 2008, Bioinform..

Statistical inference of the mechanisms driving collective cell movement

Dirk Husmeier, Jason Matthiopoulos, Robert H. Insall, 2017 .

Empirical Evaluation of Bayesian Sampling for Neural Classifiers

Stephen J. Roberts, Dirk Husmeier, William D. Penny, 1998 .

Nonhomogeneous dynamic Bayesian networks in systems biology.

Dirk Husmeier, Frank Dondelinger, Sophie Lèbre, 2012, Methods in molecular biology.

Modelling Non‐homogeneous Dynamic Bayesian Networks with Piecewise Linear Regression Models

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2019, Handbook of Statistical Genomics.

Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models

Dirk Husmeier, D. Husmeier, 2005, ECCB/JBI.

Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation

Dirk Husmeier, L. Mihaela Paun, Mitchel J. Colebank, 2020, Journal of the Royal Society Interface.

Inference of Circadian Regulatory Networks

Marco Grzegorczyk, Dirk Husmeier, V. Anne Smith, 2014, IWBBIO.

Modelling Conditional Probability Densities with Neural Networks

Dirk Husmeier, D. Husmeier, 1997 .

Detecting recombination with MCMC

Dirk Husmeier, Gráinne McGuire, 2002, ISMB.

Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2013, Machine Learning.

Direct Learning Left Ventricular Meshes from CMR Images

Dirk Husmeier, Lukasz Romaszko, Xiaoyu Luo, 2019 .

Random Vector Functional Link (RVFL) Networks

Dirk Husmeier, D. Husmeier, 1999 .

Improving the identification of antigenic sites in the H1N1 influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model

Dirk Husmeier, Vinny Davies, Richard Reeve, 2019, Journal of the Royal Statistical Society. Series C, Applied statistics.

Introduction to Statistical Phylogenetics

Dirk Husmeier, D. Husmeier, 2005 .

Inference of Circadian Regulatory Pathways Based on Delay Differential Equations

Dirk Husmeier, Catherine F. Higham, D. Husmeier, 2015, IWBBIO.

Bayesian Approaches to Gaussian Mixture Modeling

William D. Penny, Stephen J. Roberts, Dirk Husmeier, 1998, IEEE Trans. Pattern Anal. Mach. Intell..

Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration

Marco Grzegorczyk, Dirk Husmeier, Andrej Aderhold, 2017, Computational Statistics.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

Dirk Husmeier, V. Anne Smith, Colin M. Beale, 2012, Ecol. Informatics.

Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models

Dirk Husmeier, L. Mihaela Paun, D. Husmeier, 2021, Statistics and Computing.

Graph Neural Network Emulation of Cardiac Mechanics

Hao Gao, Dirk Husmeier, Arash Rabbani, 2021, Proceedings of the 3rd International Conference on Statistics: Theory and Applications.

Generalized functional responses in habitat selection fitted by decision trees and random forests

Dirk Husmeier, Jason Matthiopoulos, Shaykhah Aldossari, 2021 .

Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics

Colin Berry, Hao Gao, Dirk Husmeier, 2021, Artif. Intell. Medicine.

A computational study of pulmonary hemodynamics in healthy and hypoxic mice

Dirk Husmeier, Naomi C. Chesler, M. Umar Qureshi, 2017, 1712.01699.

Biological Network Inference at Multiple Scales: From Gene Regulation to Species Interactions

Dirk Husmeier, V. Anne Smith, Andrej Aderhold, 2015 .

Changes and classification in myocardial contractile function in the left ventricle following acute myocardial infarction

Colin Berry, Hao Gao, Dirk Husmeier, 2017, Journal of The Royal Society Interface.

Discussion on the meeting on ‘Statistical modelling and analysis of genetic data’

Terence P. Speed, Peter Donnelly, Dirk Husmeier, 2002 .

A universal approximator network for learning conditional probability densities

Dirk Husmeier, John G. Taylor, D. Husmeier, 1997 .

MCMC with delayed acceptance using a surrogate model with an application to cardiovascular fluid dynamics

Dirk Husmeier, L. Mihaela Paun, Umar Qureshi, 2019, Proceedings of the International Conference on Statistics: Theory and Applications.

Statistical Applications in Genetics and Molecular Biology Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments

Alexander V. Mantzaris, Dirk Husmeier, D. Husmeier, 2011 .

Advances in computational modelling for personalised medicine after myocardial infarction

Colin Berry, Hao Gao, Dirk Husmeier, 2017, Heart.

Hemodynamic assessment of pulmonary hypertension in mice: a model-based analysis of the disease mechanism

Dirk Husmeier, L. Mihaela Paun, Mitchel J. Colebank, 2017, Biomechanics and modeling in mechanobiology.

A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2012, Statistical applications in genetics and molecular biology.

Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks.

Dirk Husmeier, D. Husmeier, A. Werhli, 2007, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.

Predicting ecological regime shift under climate change: New modelling techniques and potential of molecular-based approaches

Dirk Husmeier, V. Anne Smith, Richard Stafford, 2013 .

Predicting ecological regime shift under climate change : new modelling and molecular-based approaches

Dirk Husmeier, V. Anne Smith, Richard Stafford, 2013 .

Contribution to the discussion on the paper by Handcock, Raftery and Tantrum: Model-based clustering for social networks

Dirk Husmeier, Chris A. Glasbey, 2007 .

Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data.

Marco Grzegorczyk, Dirk Husmeier, Andrej Aderhold, 2018, Methods in molecular biology.