Professor Dirk Husmeier

Chair of Statistics (Statistics)

telephone: 01413305141
email: Dirk.Husmeier@glasgow.ac.uk

Mathematics & Statistics, Room 336

Research interests

My current reseach focuses on parameter inference and uncertainty quantificaiton in complex biological systems, using methods from machine learning, computational statistics and emulation.

Research groups

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998 | 1997 | 1996 | 1992

Number of items: 199.

2024

Ge, Y., Husmeier, D. , Rabbani, A. and Gao, H. (2024) Advanced statistical inference of myocardial stiffness: A time series Gaussian Process approach of emulating cardiac mechanics for real-time clinical decision support. Computers in Biology and Medicine, (Accepted for Publication)

Paun, L. M., Colebank, M. J. and Husmeier, D. (2024) A comparison of Gaussian processes and polynomial chaos emulators in the context of haemodynamic pulse-wave propagation modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, (Accepted for Publication)

Paun, L. M. et al. (2024) SECRET: Statistical Emulation for Computational Reverse Engineering and Translation with applications in healthcare. Computer Methods in Applied Mechanics and Engineering, 430, 117193. (doi: 10.1016/j.cma.2024.117193)

Dalton, D., Lazarus, A., Gao, H. and Husmeier, D. (2024) Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations. Journal of Machine Learning Research, (Accepted for Publication)

Colebank, M. J., Oomen, P. A., Witzenburg, C. M., Grosberg, A., Beard, D. A., Husmeier, D. , Olufsen, M. S. and Chesler, N. C. (2024) Guidelines for mechanistic modeling and analysis in cardiovascular research. American Journal of Physiology - Heart and Circulatory Physiology, 327(2), H473-H503. (doi: 10.1152/ajpheart.00766.2023) (PMID:38904851) (PMCID:PMC11442102)

Paun, M., Fensterseifer Schmidt, A., Mcginty, S. and Husmeier, D. (2024) Constrained Bayesian optimization with a cardiovascular application. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 480(2295), 20230371. (doi: 10.1098/rspa.2023.0371)

Bartolo, M. A. et al. (2024) Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images. Journal of Physiology, 602(16), pp. 3929-3954. (doi: 10.1113/JP286193) (PMID:39075725)

Dalton, D., Husmeier, D. and Gao, H. (2024) Physics and Lie Symmetry Informed Gaussian Processes. In: 41st International Conference on Machine Learning, Vienna, Austria, 21-27 Jul 2024, (Accepted for Publication)

Chadwick, F. J., Haydon, D. T. , Husmeier, D. , Ovaskainen, O. and Matthiopoulos, J. (2024) LIES of omission: complex observation processes in ecology. Trends in Ecology and Evolution, 39(4), pp. 368-380. (doi: 10.1016/j.tree.2023.10.009) (PMID:37949794)

Yang, Y., Husmeier, D. , Gao, H. , Berry, C. , Carrick, D. and Radjenovic, A. (2024) Automatic detection of myocardial ischaemia using generalisable spatio-temporal hierarchical Bayesian modelling of DCE-MRI. Computerized Medical Imaging and Graphics, 113, 102333. (doi: 10.1016/j.compmedimag.2024.102333) (PMID:38281420)

2023

Zhou, J., Husmeier, D. , Gao, H. , Yin, C., Qiu, C., Jing, X., Qi, Y. and Liu, W. (2023) Bayesian inversion of frequency-domain airborne EM data with spatial correlation prior information. IEEE Transactions on Geoscience and Remote Sensing, 62, 2000816. (doi: 10.1109/TGRS.2023.3344946)

Dalton, D., Husmeier, D. and Gao, H. (2023) Physics-informed graph neural network emulation of soft-tissue mechanics computer methods in applied mechanics and engineering. Computer Methods in Applied Mechanics and Engineering, 417(A), 116351. (doi: 10.1016/j.cma.2023.116351)

Ge, Y., Husmeier, D. , Lazarus, A., Rabbani, A. and Gao, H. (2023) Bayesian inference of cardiac models emulated with a time series Gaussian process. In: Proceedings of the 5th International Conference on Statistics: Theory and Applications. International Aset Inc., p. 149. ISBN 9781990800252 (doi: 10.11159/icsta23.149)

Rabbani, A., Gao, H. , Lazarus, A., Dalton, D., Ge, Y., Mangion, K., Berry, C. and Husmeier, D. (2023) Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications. Computerized Medical Imaging and Graphics, 106, 102203. (doi: 10.1016/j.compmedimag.2023.102203) (PMID:36848766)

Paun, I. , Husmeier, D. and Torney, C. J. (2023) Stochastic variational inference for scalable non-stationary Gaussian process regression. Statistics and Computing, 33(2), 44. (doi: 10.1007/s11222-023-10210-w)

Harvey, W. T., Davies, V. , Daniels, R. S., Whittaker, L., Gregory, V., Hay, A. J., Husmeier, D. , McCauley, J. W. and Reeve, R. (2023) A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. PLoS Computational Biology, 19(3), e1010885. (doi: 10.1371/journal.pcbi.1010885) (PMID:36972311) (PMCID:PMC10079231)

Tragakis, A., Kaul, C., Murray-Smith, R. and Husmeier, D. (2023) The Fully Convolutional Transformer for Medical Image Segmentation. In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2023), Waikoloa, HI, USA, 03-07 Jan 2023, pp. 1022-1031. ISBN 9781665493468 (doi: 10.1109/WACV56688.2023.00365)

Gaskell, J. , Campioni, N., Morales, J. M. , Husmeier, D. and Torney, C. J. (2023) Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation. Journal of the Royal Society: Interface, 20(198), 20220676. (doi: 10.1098/rsif.2022.0676) (PMID:36596456)

Ryan, W. , Husmeier, D. , Rolinski, O. J. and Vyshemirsky, V. (2023) Bayesian Model Selection and Emulation for Protein Fluorescence. In: 5th International Conference on Statistics: Theory and Applications (ICSTA'23), London, UK, 3-5 Aug 2023, ISBN 9781990800252 (doi: 10.11159/icsta23.153)

2022

Paun, I. , Husmeier, D. , Hopcraft, J. G. C. , Masolele, M. M. and Torney, C. J. (2022) Inferring spatially varying animal movement characteristics using a hierarchical continuous-time velocity model. Ecology Letters, 25(12), pp. 2726-2738. (doi: 10.1111/ele.14117) (PMID:36256526)

Aldossari, S., Husmeier, D. and Matthiopoulos, J. (2022) Transferable species distribution modelling: comparative performance of Generalised Functional Response models. Ecological Informatics, 71, 101803. (doi: 10.1016/j.ecoinf.2022.101803)

Dalton, D., Gao, H. and Husmeier, D. (2022) Emulation of cardiac mechanics using Graph Neural Networks. Computer Methods in Applied Mechanics and Engineering, 401(B), 115645. (doi: 10.1016/j.cma.2022.115645)

Yang, Y. , Gao, H. , Berry, C. , Carrick, D., Radjenovic, A. and Husmeier, D. (2022) Classification of myocardial blood flow based on dynamic contrast-enhanced magnetic resonance imaging using hierarchical Bayesian models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(5), pp. 1085-1115. (doi: 10.1111/rssc.12568)

Lazarus, A., Gao, H. , Luo, X. and Husmeier, D. (2022) Improving cardio-mechanic inference by combining in vivo strain data with ex vivo volume–pressure data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(4), pp. 906-931. (doi: 10.1111/rssc.12560)

Husmeier, D. , Dalton, D., Lazarus, A. and Gao, H. (2022) Forward and Inverse Uncertainty Quantification in Cardiac Mechanics. 4th International Conference on Statistics: Theory and Applications (ICSTA 22), Prague, Czech Republic, 28-30 July 2022. (doi: 10.11159/icsta22.161)

Paun, L. M., Schmidt, A. F., Mcginty, S. and Husmeier, D. (2022) Statistical Inference for Optimisation of Drug Delivery from Stents. In: Proceedings of the 4th International Conference on Statistics: Theory and Applications (ICSTA 22), Prague, Czech Republic, 28-30 July 2022, ISBN 9781990800085 (doi: 10.11159/icsta22.138)

Yang, Y., Gao, H. , Berry, C. , Radjenovic, A. and Husmeier, D. (2022) Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model. In: Proceedings of the 4th International Conference on Statistics: Theory and Applications (ICSTA 22), Prague, Czech Republic, 28-30 July 2022, ISBN 9781990800085 (doi: 10.11159/icsta22.146)

Lazarus, A., Dalton, D., Husmeier, D. and Gao, H. (2022) Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics. Biomechanics and Modeling in Mechanobiology, 21(3), pp. 953-982. (doi: 10.1007/s10237-022-01571-8) (PMID:35377030) (PMCID:PMC9132878)

Chadwick, F. J. et al. (2022) Combining rapid antigen testing and syndromic surveillance improves community-based COVID-19 detection in a low-income country. Nature Communications, 13, 2877. (doi: 10.1038/s41467-022-30640-w) (PMID:35618714) (PMCID:PMC9135686)

Borowska, A. , Gao, H. , Lazarus, A. and Husmeier, D. (2022) Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle. International Journal for Numerical Methods in Biomedical Engineering, 38(5), e3593. (doi: 10.1002/cnm.3593) (PMID:35302293)

Paun, L. M. and Husmeier, D. (2022) Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models. Statistics and Computing, 32(1), 1. (doi: 10.1007/s11222-021-10060-4)

2021

Morrow, A. et al. (2021) Rationale and design of the Medical Research Council Precision medicine with Zibotentan in microvascular angina (PRIZE) trial MRI sub-study. British Society of Cardiovascular Magnetic Resonance 2021 Annual Meeting, 12 October 2021. A2.1-A2. (doi: 10.1136/heartjnl-2021-BSCMR.3)

Campioni, N., Husmeier, D. , Morales, J. , Gaskell, J. and Torney, C. J. (2021) Inferring microscale properties of interacting systems from macroscale observations. Physical Review Research, 3(4), 043074. (doi: 10.1103/PhysRevResearch.3.043074)

Romaszko, L., Borowska, A. , Lazarus, A., Dalton, D., Berry, C. , Luo, X. , Husmeier, D. and Gao, H. (2021) Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. Artificial Intelligence In Medicine, 119, 102140. (doi: 10.1016/j.artmed.2021.102140)

Dalton, D., Lazarus, A., Rabbani, A., Gao, H. and Husmeier, D. (2021) Graph Neural Network Emulation of Cardiac Mechanics. In: 3rd International Conference on Statistics: Theory and Applications (ICSTA'21), 29-31 Jul 2021, p. 127. ISBN 9781927877913 (doi: 10.11159/icsta21.127)

Paun, L. M., Borowska, A. , Colebank, M. J., Olufsen, M. S. and Husmeier, D. (2021) Inference in Cardiovascular Modelling Subject to Medical Interventions. In: 3rd International Conference on Statistics: Theory and Applications (ICSTA'21), 29-31 Jul 2021, p. 109. ISBN 9781927877913 (doi: 10.11159/icsta21.109)

Aldossari, S., Husmeier, D. and Matthiopoulos, J. (2021) Generalized functional responses in habitat selection fitted by decision trees and random forests. In: Ladde, G. and Samia, N. (eds.) Proceedings of the 3rd International Conference on Statistics: Theory and Applications (ICSTA'21). Avestia Publishing: Ottawa, Canada, p. 125. ISBN 9781927877913 (doi: 10.11159/icsta21.125)

Borowska, A. , Giurghita, D. and Husmeier, D. (2021) Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis. Journal of Computational Physics, 429, 109999. (doi: 10.1016/j.jcp.2020.109999)

Hanlon, P. et al. (2021) COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study. Wellcome Open Research, 5, 75. (doi: 10.12688/wellcomeopenres.15849.3) (PMID:33709037) (PMCID:PMC7927210)

Niu, M., Wandy, J. , Daly, R. , Rogers, S. and Husmeier, D. (2021) R package for statistical inference in dynamical systems using kernel based gradient matching: KGode. Computational Statistics, 36(1), pp. 715-747. (doi: 10.1007/s00180-020-01014-x)

Torney, C. J. , Morales, J. M. and Husmeier, D. (2021) A hierarchical machine learning framework for the analysis of large scale animal movement data. Movement Ecology, 9, 6. (doi: 10.1186/s40462-021-00242-0) (PMID:33602302)

Paun, L. M. and Husmeier, D. (2021) Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid‐dynamics model of the pulmonary circulation. International Journal for Numerical Methods in Biomedical Engineering, 37(2), e3421. (doi: 10.1002/cnm.3421) (PMID:33249755) (PMCID:PMC7901000)

2020

Paun, L. M., Colebank, M. J., Olufsen, M. S., Hill, N. A. and Husmeier, D. (2020) Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation. Journal of the Royal Society: Interface, 17(173), 20200886. (doi: 10.1098/rsif.2020.0886) (PMID:33353505)

Morrow, A. J. et al. (2020) Rationale and design of the Medical Research Council precision medicine with Zibotentan in microvascular angina (PRIZE) trial. American Heart Journal, 229, pp. 70-80. (doi: 10.1016/j.ahj.2020.07.007) (PMID:32942043)

Campioni, N., Husmeier, D. , Morales, J. M. , Gaskell, J. and Torney, C. J. (2020) Modelling multiscale collective behavior with Gaussian processes. In: Ladde, G. and Samia, N. (eds.) Proceedings of the 2nd International Conference on Statistics: Theory and Applications (ICSTA'20). Avestia Publishing: Ottawa, Canada, p. 124. ISBN 9781927877685 (doi: 10.11159/icsta20.124)

Dalton, D., Lazarus, A. and Husmeier, D. (2020) Comparative evaluation of different emulators for cardiac mechanics. In: Ladde, G. and Noelle, S. (eds.) Proceedings of the 2nd International Conference on Statistics: Theory and Applications (ICSTA'20). Avestia Publishing: Ottawa, Canada, p. 126. ISBN 9781927877685 (doi: 10.11159/icsta20.126)

Gaskell, J. , Campioni, N., Morales, J. M. , Husmeier, D. and Torney, C. J. (2020) Approximate Bayesian inference for individual-based models with emergent dynamics. In: Ladde, G. and Samia, N. (eds.) Proceedings of the 2nd International Conference on Statistics: Theory and Applications (ICSTA'20). Avestia Publishing: Ottawa, Canada, p. 125. ISBN 9781927877685 (doi: 10.11159/icsta20.125)

Husmeier, D. and Paun, L. M. (2020) Closed-loop effects in cardiovascular clinical decision support. In: Ladde, G. and Samia, N. (eds.) Proceedings of the 2nd International Conference on Statistics: Theory and Applications (ICSTA'20). Avestia Publishing: Ottawa, Canada, p. 128. ISBN 9781927877685 (doi: 10.11159/icsta20.128)

Aldossari, S., Matthiopoulos, J. and Husmeier, D. (2020) Statistical Modelling of Habitat Selection. In: Irigoien, I., Lee, D.-J., Martínez-Minaya, J. and Rodríguez-Álvarez, M. X. (eds.) Proceedings of the 35th International Workshop on Statistical Modelling. Servicio Editorial de la Universidad del País Vasco: Bilbao, Spain, pp. 275-279. ISBN 9788413192673

Dalton, D. and Husmeier, D. (2020) Improved statistical emulation for a soft-tissue cardiac mechanical model. In: Irigoien, I., Lee, D.-J., Martínez-Minaya, J. and Rodríguez-Álvarez, M. X. (eds.) Proceedings of the 35th International Workshop on Statistical Modelling. Servicio Editorial de la Universidad del País Vasco: Bilbao, Spain, pp. 55-60. ISBN 9788413192673

Husmeier, D. and Paun, L. M. (2020) Closed-loop effects in coupling cardiac physiological models to clinical interventions. In: Irigoien, I., Lee, D.-J., Martínez-Minaya, J. and Rodríguez-Álvarez, M. X. (eds.) Proceedings of the 35th International Workshop on Statistical Modelling. Servicio Editorial de la Universidad del País Vasco: Bilbao, Spain, pp. 120-125. ISBN 9788413192673

Li, W. et al. (2020) Analysis of cardiac amyloidosis progression using model-based markers. Frontiers in Physiology, 11, 324. (doi: 10.3389/fphys.2020.00324) (PMID:32425806) (PMCID:PMC7203577)

2019

Davies, V. , Noè, U. , Lazarus, A., Gao, H. , Macdonald, B. , Berry, C. , Luo, X. and Husmeier, D. (2019) Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(5), pp. 1555-1576. (doi: 10.1111/rssc.12374) (PMID:31762497) (PMCID:PMC6856984)

Colebank, M. J., Paun, L. M., Qureshi, M. U., Chesler, N., Husmeier, D. , Olufsen, M. S. and Ellwein Fix, L. (2019) Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries. Journal of the Royal Society: Interface, 16, 20190284. (doi: 10.1098/rsif.2019.0284)

Grzegorczyk, M. and Husmeier, D. (2019) Modelling non-homogeneous dynamic Bayesian networks with piece-wise linear regression models. In: Balding, D. J., Moltke, I. and Marioni, J. (eds.) Handbook of Statistical Genomics. Wiley: Hoboken, NJ, pp. 899-932. ISBN 9781119429142

Macdonald, B. and Husmeier, D. (2019) Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching. Statistics and Computing, 29(5), pp. 853-867. (doi: 10.1007/s11222-018-9840-4)

Husmeier, D. , Lazarus, A., Noè, U. , Davies, V. , Borowska, A. , Macdonald, B. , Gao, H. , Berry, C. and Luo, X. (2019) Statistical Emulation of Cardiac Mechanics: an Important Step Towards a Clinical Decision Support System. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 29. ISBN 9781927877647 (doi: 10.11159/icsta19.29)

Paun, I. , Husmeier, D. and Torney, C. (2019) A Study on Discrete-Time Movement Models. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 27. ISBN 9781927877647 (doi: 10.11159/icsta19.27)

Paun, L. M., Colebank, M., Qureshi, M. U., Olufsen, M., Hill, N. and Husmeier, D. (2019) MCMC with Delayed Acceptance using a Surrogate Model with an Application to Cardiovascular Fluid Dynamics. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 28. ISBN 9781927877647 (doi: 10.11159/icsta19.28)

Romaszko, L., Borowska, A. , Lazarus, A., Gao, H. , Luo, X. and Husmeier, D. (2019) Direct Learning Left Ventricular Meshes from CMR Images. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 25. ISBN 9781927877647 (doi: 10.11159/icsta19.25)

Romaszko, L., Lazarus, A., Gao, H. , Borowska, A. , Luo, X. and Husmeier, D. (2019) Massive Dimensionality Reduction for the Left Ventricular Mesh. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 24. ISBN 9781927877647 (doi: 10.11159/icsta19.24)

Yang, Y. , Gao, H. , Berry, C. , Radjenovic, A. and Husmeier, D. (2019) Quantification of Myocardial Perfusion Lesions Using Spatially Variant Finite Mixture Modelling of DCE-MRI. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 26. ISBN 9781927877647 (doi: 10.11159/icsta19.26)

Davies, V. , Harvey, W. T., Reeve, R. and Husmeier, D. (2019) Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(4), pp. 859-885. (doi: 10.1111/rssc.12338) (PMID:31598013) (PMCID:PMC6774336)

Devlin, J., Husmeier, D. and Mackenzie, J.A. (2019) Optimal estimation of drift and diffusion coefficients in the presence of static localization error. Physical Review E, 100, 022134. (doi: 10.1103/PhysRevE.100.022134)

Noè, U. , Lazarus, A., Gao, H. , Davies, V. , Macdonald, B. , Mangion, K., Berry, C. , Luo, X. and Husmeier, D. (2019) Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance. Journal of the Royal Society: Interface, 16(156), 20190114. (doi: 10.1098/rsif.2019.0114) (PMID:31266415) (PMCID:PMC6685034)

Umar Qureshi, M., Colebank, M. J., Paun, L. M., Ellwein, L., Chesler, N., Haider, M. A., Hill, N. A. , Husmeier, D. and Olufsen, M. S. (2019) Hemodynamic assessment of pulmonary hypertension in mice: a model based analysis of the disease mechanism. Biomechanics and Modeling in Mechanobiology, 18(1), pp. 219-243. (doi: 10.1007/s10237-018-1078-8) (PMID:30284059)

Grzegorczyk, M., Aderhold, A. and Husmeier, D. (2019) Overview and evaluation of recent methods for statistical inference of gene regulatory networks from time series data. In: Sanguinetti, G. and Huynh-Thu, V. A. (eds.) Gene Regulatory Networks: Methods and Protocols. Series: Methods in molecular biology (1883). Humana Press: New York, NY, pp. 49-94. (doi: 10.1007/978-1-4939-8882-2_3)

2018

Giurghita, D. and Husmeier, D. (2018) Statistical modelling of cell movement. Statistica Neerlandica, 72(3), pp. 265-280. (doi: 10.1111/stan.12140)

Păun, L. M., Qureshi, M. U., Colebank, M., Hill, N. A. , Olufsen, M. S., Haider, M. A. and Husmeier, D. (2018) MCMC methods for inference in a mathematical model of pulmonary circulation. Statistica Neerlandica, 72(3), pp. 306-338. (doi: 10.1111/stan.12132)

Wandy, J. , Niu, M., Giurghita, D., Daly, R. , Rogers, S. and Husmeier, D. (2018) ShinyKGode: an interactive application for ODE parameter inference using gradient matching. Bioinformatics, 34(13), pp. 2314-2315. (doi: 10.1093/bioinformatics/bty089) (PMID:29490021) (PMCID:PMC6022662)

Niu, M., Macdonald, B. , Rogers, S. , Filippone, M. and Husmeier, D. (2018) Statistical inference in mechanistic models: time warping for improved gradient matching. Computational Statistics, 33(2), pp. 1091-1123. (doi: 10.1007/s00180-017-0753-z)

Mangion, K., Gao, H. , Husmeier, D. , Luo, X. and Berry, C. (2018) Advances in computational modelling for personalised medicine after myocardial infarction. Heart, 104(7), pp. 550-557. (doi: 10.1136/heartjnl-2017-311449) (PMID:29127185)

Lazarus, A., Husmeier, D. and Papamarkou, T. (2018) Multiphase MCMC sampling for parameter inference in nonlinear ordinary differential equations. Proceedings of Machine Learning Research, 84, pp. 1252-1260.

2017

Gao, H. , Mangion, K., Carrick, D., Husmeier, D. , Luo, X. and Berry, C. (2017) Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models. Scientific Reports, 7, 13527. (doi: 10.1038/s41598-017-13635-2) (PMID:29051544) (PMCID:PMC5648923)

Giurghita, D. and Husmeier, D. (2017) Statistical Modelling of Cell Movement Data Using the Unscented Kalman Filter. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Husmeier, D. , Ferguson, E. , Matthiopoulos, J. and Insall, R. (2017) Statistical Inference of the Drivers of Collective Cell Movement. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Lazarus, A., Husmeier, D. and Papamarkou, T. (2017) Inference in Complex Systems Using Multi-Phase MCMC Sampling with Gradient Matching. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2017) Parameter Inference in Differential Equation Models Using Time Warped Gradient Matching. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Pasetto, M., Noè, U. , Luati, A. and Husmeier, D. (2017) Inference on the Duffing System With the Unscented Kalman Filter and Optimization of Sigma Points. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Paun, L., Haider, M., Hill, N. , Olufsen, M., Qureshi, M., Papamarkou, T. and Husmeier, D. (2017) Parameter Inference in the Pulmonary Blood Circulation. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Stewart, K., Matthews, L. , Scott, E. M. , Husmeier, D. and Mccowan, C. (2017) Preliminary Investigation of the Influences on Antimicrobial Resistance. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Davies, V. , Reeve, R. , Harvey, W. T., Maree, F. F. and Husmeier, D. (2017) A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution. Computational Statistics, 32(3), pp. 803-843. (doi: 10.1007/s00180-017-0730-6)

Ferguson, E. A. , Matthiopoulos, J. , Insall, R. H. and Husmeier, D. (2017) Statistical inference of the mechanisms driving collective cell movement. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66(4), pp. 869-890. (doi: 10.1111/rssc.12203)

Ferguson, E. A. , Matthiopoulos, J. and Husmeier, D. (2017) Constructing Wildebeest Density Distributions by Spatio-temporal Smoothing of Ordinal Categorical Data Using GAMs. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 70-75.

Giurghita, D. and Husmeier, D. (2017) Statistical Modelling of Cell Movement. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 317-322.

Lazarus, A., Husmeier, D. and Papamarkou, T. (2017) Inference in Complex Systems Using Multi-Phase MCMC Sampling With Gradient Matching Burn-in. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 52-57.

Pasetto, M. E., Husmeier, D. , Noè, U. and Luati, A. (2017) Statistical Inference in the Duffing System with the Unscented Kalman Filter. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 119-122.

Paun, L. M., Qureshi, M. U., Colebank, M., Haider, M. A., Olufsen, M. S., Hill, N. A. and Husmeier, D. (2017) Parameter Inference in the Pulmonary Circulation of Mice. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 190-195.

Aderhold, A., Husmeier, D. and Grzegorczyk, M. (2017) Approximate Bayesian inference in semi-mechanistic models. Statistics and Computing, 27(4), pp. 1003-1040. (doi: 10.1007/s11222-016-9668-8)

Gao, H. , Aderhold, A., Mangion, K., Luo, X. , Husmeier, D. and Berry, C. (2017) Changes and classification in myocardial contractile function in the left ventricle following acute myocardial infarction. Journal of the Royal Society: Interface, 14(132), 20170203. (doi: 10.1098/rsif.2017.0203) (PMID:28747397) (PMCID:PMC5550971)

Grzegorczyk, M., Aderhold, A. and Husmeier, D. (2017) Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration. Computational Statistics, 32(2), pp. 717-761. (doi: 10.1007/s00180-017-0721-7)

Liu, Z., Macdonald, B. , Husmeier, D. and Giurghita, D. (2017) Estimating Parameters of Partial Differential Equations with Gradient Matching. Other. University of Glasgow. (Unpublished)

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2017) Parameter Inference in Differential Equation Models of Biopathways using Time Warped Gradient Matching. In: 13th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, Stirling, UK, 01-03 Sep 2016, pp. 145-159. ISBN 9783319678337 (doi: 10.1007/978-3-319-67834-4_12)

Noè, U., Chen, W. W., Filippone, M., Hill, N. and Husmeier, D. (2017) Inference in a Partial Differential Equations Model of Pulmonary Arterial and Venous Blood Circulation using Statistical Emulation. In: 13th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, Stirling, UK, 01-03 Sep 2016, pp. 184-198. ISBN 9783319678337 (doi: 10.1007/978-3-319-67834-4_15)

2016

Ferguson, E. A. , Matthiopoulos, J. , Insall, R. H. and Husmeier, D. (2016) Inference of the drivers of collective movement in two cell types: Dictyostelium and melanoma. Journal of the Royal Society: Interface, 13(123), 20160695. (doi: 10.1098/rsif.2016.0695) (PMID:27798280)

Giurghita, D. and Husmeier, D. (2016) Inference in Nonlinear Systems with Unscented Kalman Filters. In: 22nd International Conference on Computational Statistics (COMPSTAT 2016), Oviedo, Spain, 23-26 Aug 2016, pp. 383-393. ISBN 9789073592360

Macdonald, B., Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2016) Approximate parameter inference in systems biology using gradient matching: a comparative evaluation. BioMedical Engineering OnLine, 15, 80. (doi: 10.1186/s12938-016-0186-x) (PMID:27454253) (PMCID:PMC4959362)

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2016) Fast inference in nonlinear dynamical systems using gradient matching. Proceedings of Machine Learning Research, 48, pp. 1699-1707.

Aderhold, A., Smith, V. A. and Husmeier, D. (2016) Biological network inference at multiple scales: from gene regulation to species interactions. In: Elloumi, M., Iliopoulos, C. S., Wang, J. T.L. and Zomaya, A. Y. (eds.) Pattern Recognition in Computational Molecular Biology. Series: Wiley series on bioinformatics: computational techniques and engineering. John Wiley & Sons: New Jersey, United States, pp. 525-554. ISBN 9781118893685

Davies, V. , Reeve, R. , Harvey, W. T. and Husmeier, D. (2016) Selecting random effect components in a sparse hierarchical Bayesian model for identifying antigenic variability. In: Computational Intelligence Methods for Bioinformatics and Biostatistics: 12th International Meeting, CIBB 2015, Naples, Italy, September 10-12, 2015, Revised Selected Papers. Series: Lecture Notes in Computer Science (9874). Springer, pp. 14-27. ISBN 9783319443317 (doi: 10.1007/978-3-319-44332-4_2)

2015

Macdonald, B. and Husmeier, D. (2015) Gradient matching methods for computational inference in mechanistic models for systems biology: a review and comparative analysis. Frontiers in Bioengineering and Biotechnology, 3, 180. (doi: 10.3389/fbioe.2015.00180) (PMID:26636071) (PMCID:PMC4654429)

Grzegorczyk, M., Aderhold, A. and Husmeier, D. (2015) Network Reconstruction with Realistic Models. In: 30th International Workshop on Statistical Modelling, Linz, Austria, 06-10 Jul 2015,

Macdonald, B., Higham, C. and Husmeier, D. (2015) Controversy in mechanistic modelling with Gaussian processes. Proceedings of Machine Learning Research, 37, pp. 1539-1547.

Niu, M., Filippone, M., Husmeier, D. and Rogers, S. (2015) Inference in Nonlinear Differential Equations. In: 30th International Workshop on Statistical Modelling, Linz, Austria, 06-10 Jul 2015, pp. 187-190.

Noè, U., Filippone, M. and Husmeier, D. (2015) Emulation of ODEs with Gaussian Processes. In: 30th International Workshop on Statistical Modelling, Linz, Austria, 06-10 Jul 2015, pp. 191-194.

Grzegorczyk, M., Aderhold, A. and Husmeier, D. (2015) Inferring bi-directional interactions between circadian clock genes and metabolism with model ensembles. Statistical Applications in Genetics and Molecular Biology, 14(2), pp. 143-167. (doi: 10.1515/sagmb-2014-0041) (PMID:25719342)

Macdonald, B. and Husmeier, D. (2015) Computational inference in systems biology. In: Ortuño, F. and Rojas, I. (eds.) Bioinformatics and Biomedical Engineering:Third International Conference, IWBBIO 2015, Granada, Spain, April 15-17, 2015: Proceedings, Part II. Series: Lecture Notes in Computer Science (9044). Springer, pp. 276-288. ISBN 9783319164809 (doi: 10.1007/978-3-319-16480-9_28)

Higham, C. F. and Husmeier, D. (2015) Inference of circadian regulatory pathways based on delay differential equations. In: Ortuno, F. and Ignacio, R. (eds.) Bioinformatics and Biomedical Engineering. Series: Lecture Notes in Computer Science, 9044 (9044). Springer, pp. 468-478. ISBN 9783319164793 (doi: 10.1007/978-3-319-16480-9_46)

2014

Aderhold, A., Husmeier, D. and Grzegorczyk, M. (2014) Statistical inference of regulatory networks for circadian regulation. Statistical Applications in Genetics and Molecular Biology, 13(3), pp. 227-273. (doi: 10.1515/sagmb-2013-0051)

Davies, V. , Reeve, R. , Harvey, W., Maree, F. and Husmeier, D. (2014) Sparse Bayesian variable selection for the identiﬁcation of antigenic variability in the Foot-and-Mouth disease virus. Proceedings of Machine Learning Research, 33, pp. 149-158.

Grzegorczyk, M., Aderhold, A., Smith, V. A. and Husmeier, D. (2014) Inference of circadian regulatory networks. In: Ortuno, F. and Rojas, I. (eds.) International Work-Conference on Bioinformatics and Biomedical Engineering. Copicentro Granada S.L: Granada, pp. 1001-1014. ISBN 9788415814849

Davies, V. and Husmeier, D. (2014) Modelling transcriptional regulation with Gaussian processes. In: Valente, A. X.C.N., Sarkar, A. and Gao, Y. (eds.) Recent Advances in Systems Biology Research. Nova Science Publishers: New York, pp. 157-184. ISBN 9781629487366

2013

Higham, C. and Husmeier, D. (2013) A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana. BMC Bioinformatics, 14(Sup 10), S3. (doi: 10.1186/1471-2105-14-S10-S3)

Grzegorczyk, M. and Husmeier, D. (2013) Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models. Machine Learning, 91(1), pp. 105-154. (doi: 10.1007/s10994-012-5326-3)

Dondelinger, F., Lèbre, S. and Husmeier, D. (2013) Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Machine Learning, 90(2), pp. 191-230. (doi: 10.1007/s10994-012-5311-x)

Aderhold, A., Husmeier, D. and Smith, V.A. (2013) Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes. Proceedings of Machine Learning Research, 31, pp. 75-84.

Aderhold, A., Husmeier, D. , Smith, V.A., Millar, A.J. and Grzegorczyk, M. (2013) Assessment of regression methods for inference of regulatory networks involved in circadian regulation. In: Proceedings of the 10th International Workshop on Computational Systems Biology. Tampere International Center for Signal Processing: Tampere, Finland, pp. 29-33. ISBN 9789521530913

Davies, V. and Husmeier, D. (2013) Assessing the impact of non-additive noise on modelling transcriptional regulation with Gaussian processes. In: Muggeo, V.M.R., Capursi, V., Boscaino, G. and Lovison, G. (eds.) Proceedings of the 28th International Workshop on Statistical Modelling. Gruppo Istituto Poligrafico Europeo SRL, pp. 559-562. ISBN 9788896251492

Dondelinger, F., Filippone, M., Rogers, S. and Husmeier, D. (2013) ODE parameter inference using adaptive gradient matching with Gaussian processes. Proceedings of Machine Learning Research, 31, pp. 216-228.

Macdonald, B., Dondelinger, F. and Husmeier, D. (2013) Inference in complex biological systems with Gaussian processes and parallel tempering. In: Muggeo, V.M.R., Capursi, V., Boscaino, G. and Lovison, G. (eds.) Proceedings of the 28th International Workshop on Statistical Modelling. Gruppo Istituto Poligrafico Europeo SRL, pp. 673-676. ISBN 9788896251492

Stafford, R., Smith, V.A., Husmeier, D. , Grima, T. and Guinn, B.-A. (2013) Predicting ecological regime shift under climate change: new modelling techniques and potential of molecular-based approaches. Current Zoology, 59(3), pp. 403-417.

2012

Aderhold, A., Husmeier, D. , Lennon, J.J., Beale, C.M. and Smith, V.A. (2012) Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data. Ecological Informatics, 11, pp. 55-64. (doi: 10.1016/j.ecoinf.2012.05.002)

Marbach, D. et al. (2012) Wisdom of crowds for robust gene network inference. Nature Methods, 9(8), pp. 796-804. (doi: 10.1038/nmeth.2016)

Grzegorczyk, M. and Husmeier, D. (2012) A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology. Statistical Applications in Genetics and Molecular Biology, 11(4), Art. 7. (doi: 10.1515/1544-6115.1761)

Dondelinger, F., Rogers, S. , Filippone, M., Cretella, R., Palmer, T., Smith, R., Millar, A. and Husmeier, D. (2012) Parameter inference in mechanistic models of cellular regulation and signalling pathways using gradient matching. In: WCSB2012 - 9th International Workshop on Computational Systems Biology, Ulm, Germany, 4-6 Jun 2012,

Grzegorczyk, M. and Husmeier, D. (2012) Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters. Proceedings of Machine Learning Research, 22, pp. 467-476.

Ji, R. and Husmeier, D. (2012) Warped Gaussian process modelling of transcriptional regulation. In: 9th International Workshop on Computational Systems Biology, Ulm, Germany, 4-6 Jun 2012,

Dondelinger, F., Husmeier, D. and Lebre, S. (2012) Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series. Euphytica, 183(3), pp. 361-377. (doi: 10.1007/s10681-011-0538-3)

Lebre, S., Dondelinger, F. and Husmeier, D. (2012) Nonhomogeneous dynamic Bayesian networks in systems biology. In: Wang, J., Tan, A.C. and Tian, T. (eds.) Next Generation Microarray Bioinformatics. Humana Press: New York, NYC, USA, pp. 199-213. ISBN 9781617793998 (doi: 10.1007/978-1-61779-400-1_13)

2011

Dondelinger, F., Aderhold, A., Lebre, S., Grzegorczyk, M. and Husmeier, D. (2011) A Bayesian regression and multiple changepoint model for systems biology. In: Conesa, D., Forte, A., Lopez-Quilez, A. and Munoz, F. (eds.) International Workshop on Statistical Modelling. Copiformes S.L.: Valencia, Spain, pp. 189-194. ISBN 9788469451298

Husmeier, D. (2011) Contribution to the discussion on Riemann manifold Hamiltonian Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(2), pp. 184-185. (doi: 10.1111/j.1467-9868.2010.00765.x)

Grzegorczyk, M. and Husmeier, D. (2011) Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes. Bioinformatics, 27(5), pp. 693-699. (doi: 10.1093/bioinformatics/btq711)

Grzegorczyk, M. and Husmeier, D. (2011) Non-homogeneous dynamic Bayesian networks for continuous data. Machine Learning, 83(3), pp. 355-419. (doi: 10.1007/s10994-010-5230-7)

Grzegorczyk, M., Husmeier, D. and Rahnenführer, J. (2011) Modelling non-stationary dynamic gene regulatory processes with the BGM model. Computational Statistics, 26(2), pp. 199-218. (doi: 10.1007/s00180-010-0201-9)

Husmeier, D. , Werhli, A.V. and Grzegorczyk, M. (2011) Advanced applications of Bayesian networks in systems biology. In: Stumpf, M.P.H., Balding, D.J. and Girolami, M. (eds.) Handbook of Statistical Systems Biology. Wiley: Chichester, UK, pp. 270-289. ISBN 9780470710869 (doi: 10.1002/9781119970606.ch13)

2010

Dondelinger, F., Lebre, S. and Husmeier, D. (2010) Heterogeneous continuous dynamic Bayesian networks with flexible structure and inter-time segment information sharing. In: Furnkranz, J. and Joachims, T. (eds.) International Conference on Machine Learning (ICML). Omnipress: Haifa, Israel, pp. 303-310. ISBN 9781605589077

Faisal, A., Dondelinger, F., Husmeier, D. and Beale, C.M. (2010) Inferring species interaction networks from species abundance data: a comparative evaluation of various statistical and machine learning methods. Ecological Informatics, 5(6), pp. 451-464. (doi: 10.1016/j.ecoinf.2010.06.005)

Grzegorcyzk, M., Husmeier, D. and Rahnenführer, J. (2010) Modelling nonstationary gene regulatory processes. Advances in Bioinformatics, 2010, pp. 1-17. (doi: 10.1155/2010/749848)

Husmeier, D. , Dondelinger, F. and Lebre, S. (2010) Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks. In: Advances in Neural Information Processing Systems. Series: Advances in neural information processing systems, 23 (23). Curran Associates: La Jolla, CA, USA, pp. 901-909. ISBN 9781617823800

Lin, K. and Husmeier, D. (2010) Mixtures of factor analyzers for modeling transcriptional regulation. In: Lawrence, N., Girolami, M., Rattray, M. and Sanguinetti, G. (eds.) Learning and Inference in Computational Systems Biology. Series: Computational molecular biology. MIT Press: Cambridge, MA, USA, pp. 153-200. ISBN 9780262013864

Lin, K., Husmeier, D. , Dondelinger, F., Mayer, C.D., Liu, H., Prichard, L., Salmond, G.P.C., Toth, I.K. and Birch, P.R.J. (2010) Reverse engineering gene regulatory networks related to Quorum sensing in the plant pathogen Pectobacterium Atrosepticum. In: Fenyo, D. (ed.) Computational Biology. Series: Methods in Molecular Biology (673). Humana Press: New York, NYC, USA, pp. 253-281. ISBN 9781607618416 (doi: 10.1007/978-1-60761-842-3_17)

2009

Lehrach, W.P. and Husmeier, D. (2009) Segmenting bacterial and viral DNA sequence alignments with a trans-dimensional phylogenetic factorial hidden Markov model. Journal of the Royal Statistical Society: Series C (Applied Statistics), 58(3), pp. 307-327. (doi: 10.1111/j.1467-9876.2008.00648.x)

Milne, I., Lindner, D., Bayer, M., Husmeier, D. , McGuire, G., Marshall, D. and Wright, F. (2009) TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops. Bioinformatics, 25(1), pp. 126-127. (doi: 10.1093/bioinformatics/btn575)

Grzegorczyk, M. and Husmeier, D. (2009) Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks. Lecture Notes in Computer Science, 5780, pp. 113-124. (doi: 10.1007/978-3-642-04031-3_11)

Grzegorczyk, M. and Husmeier, D. (2009) Modelling non-stationary gene regulatory processes with a non-homogeneous dynamic Bayesian network and the change point process. In: Manninen, T., Wiuf, C., Lahdesmaki, H., Grzegorczyk, M., Rahnenfuhrer, J., Ahdesmaki, M., Linne, M.L. and Yli-Harja, O. (eds.) Proceedings of the Sixth International Workshop on Computational Systems Biology (WCSB). Tampere International Centre for Signal Processing. ISBN 9789521521607

Grzegorczyk, M. and Husmeier, D. (2009) Non-stationary continuous dynamic Bayesian networks. In: Bengio, Y., Schuurmans, D., Laftery, J., Williams, C.K.I. and Culotta, A. (eds.) Advances in Neural Information Processing Systems. Series: Advances in neural information processing systems (22). Curran Associates: La Jolla, CA, USA, pp. 682-690. ISBN 9781615679119

Lin, K. and Husmeier, D. (2009) Modelling transcriptional regulation with a mixture of factor analyzers and variational Bayesian expectation maximization. EURASIP Journal on Bioinformatics and Systems Biology, 2009(601068),

Mantzaris, A.V. and Husmeier, D. (2009) Distinguishing regional from within-codon rate heterogeneity in DNA sequence alignments. Lecture Notes in Computer Science, 5780, pp. 187-198. (doi: 10.1007/978-3-642-04031-3_17)

2008

Grzegorczyk, M., Husmeier, D. , Edwards, K.D., Ghazal, P. and Millar, A.J. (2008) Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler. Bioinformatics, 24(18), pp. 2071-2078. (doi: 10.1093/bioinformatics/btn367)

Grzegorczyk, M. and Husmeier, D. (2008) Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Machine Learning, 71(2-3), pp. 265-305. (doi: 10.1007/s10994-008-5057-7)

Werhli, A. and Husmeier, D. (2008) Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions. Journal of Bioinformatics and Computational Biology, 6(3), pp. 543-572. (doi: 10.1142/S0219720008003539)

Grzegorczyk, M., Husmeier, D. and Werhli, A. (2008) Reverse engineering gene regulatory networks with various machine learning methods. In: Emmert-Streib, F. and Dehmer, M. (eds.) Analysis of Microarray Data: A Network-Based Approach. Wiley-VCH: Weinheim, Germany, pp. 101-142. ISBN 9783527318223

Husmeier, D. and Mantzaris, A. (2008) Addressing the shortcomings of three recent Bayesian methods for detecting interspecific recombination in DNA sequence alignments. Statistical Applications in Genetics and Molecular Biology, 7(1), Art. 34.

2007

Armstrong, M.R., Husmeier, D. , Phillips, M.S. and Blok, V.C. (2007) Segregation and recombination of a multipartite mitochondrial DNA in populations of the potato cyst nematode globodera pallida. Journal of Molecular Evolution, 64(6), pp. 689-701. (doi: 10.1007/s00239-007-0023-8)

Husmeier, D. and Glasbey, C. (2007) Contribution to the discussion on the paper by Handcock, Raftery and Tantrum: Model-based clustering for social networks. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(4), p. 340. (doi: 10.1111/j.1467-985X.2007.00471.x)

Husmeier, D. and Werhli, A. (2007) Bayesian integration of biological prior knowledge into the reconstruction of gene networks with Bayesian networks. In: Markstein, P. and Xu, Y. (eds.) Proceedings of the International Conference on Computational Systems Bioinformatics (CSB 2007). Imperial College Press: London, UK, pp. 85-95. ISBN 9781860948725

Lehrach, W., Husmeier, D. and Williams, C.K.I. (2007) Probabilistic in silico prediction of protein-peptide interactions. In: Eskin, W., Ideker, T., Raphael, B. and Workman, C. (eds.) Systems Biology and Regulatory Genomics. Springer: Berlin, Germany, pp. 188-197. ISBN 9783540485407

Werhli, A. and Husmeier, D. (2007) Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Statistical Applications in Genetics and Molecular Biology, 6(1), Art. 15.

2006

Werhli, A.V., Grzegorczyk, M. and Husmeier, D. (2006) Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics, 22(20), pp. 2523-2531. (doi: 10.1093/bioinformatics/btl391)

Husmeier, D. (2006) Detecting mosaic structures in DNA sequence alignments. In: Misra, J.C. (ed.) Biomathematics: Modelling and Simulation. World Scientific: Hackensack, NJ, USA, pp. 1-35. ISBN 9789812381101 (doi: 10.1142/9789812774859_0001)

Kedzierska, A. and Husmeier, D. (2006) A heuristic bayesian method for segmenting DNA sequence alignments and detecting evidence for recombination and gene conversion. Statistical Applications in Genetics and Molecular Biology, 5(1), Art. 27. (doi: 10.2202/1544-6115.1238,)

Lehrach, W. P., Husmeier, D. and Williams, C. K. I. (2006) A regularized discriminative model for the prediction of protein-peptide interactions. Bioinformatics, 22(5), pp. 532-540. (doi: 10.1093/bioinformatics/bti804)

Werhli, A., Grzegorczyk, M., Chiang, M.-T. and Husmeier, D. (2006) Improved gibbs sampling for detecting mosaic structures in DNA sequence alignments. In: Urfer, W. and Turkman, M.A. (eds.) Mosaic Structures in DNA Sequence Alignments. Centro Internacional de Matematica: Coimbra, Portugal, pp. 23-34. ISBN 9899501107

2005

Husmeier, D. (2005) Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models. Bioinformatics, 21(Suppl), ii166-ii172. (doi: 10.1093/bioinformatics/bti1127)

Husmeier, D. , Dybowski, R. and Roberts, S. (2005) Probabilistic Modeling in Bioinformatics and Medical Informatics. Series: Advanced information and knowledge processing. Springer: London. ISBN 9781852337780 (doi: 10.1007/b138794)

Husmeier, D. , Wright, F. and Milne, I. (2005) Detecting interspecific recombination with a pruned probabilistic divergence measure. Bioinformatics, 21(9), pp. 1797-1806. (doi: 10.1093/bioinformatics/bti151)

2004

Milne, I., Wright, F., Rowe, G., Marshall, D.F., Husmeier, D. and McGuire, G. (2004) TOPALi: software for automatic identification of recombinant sequences within DNA multiple alignments. Bioinformatics, 20(11), pp. 1806-1807. (doi: 10.1093/bioinformatics/bth155)

Glasbey, C. and Husmeier, D. (2004) Contribution to the discussion on the paper by Friedman and Meulman: Clustering objects on subsets of attributes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(4), pp. 840-841. (doi: 10.1111/j.1467-9868.2004.02059.x)

2003

Husmeier, D. (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 19(17), pp. 2271-2282. (doi: 10.1093/bioinformatics/btg313)

Husmeier, D. and McGuire, G. (2003) Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden markov models and markov chain monte carlo. Molecular Biology and Evolution, 20(3), pp. 315-337. (doi: 10.1093/molbev/msg039)

Husmeier, D. (2003) Reverse engineering of genetic networks with Bayesian networks. Biochemical Society Transactions, 31(6), pp. 1516-1518.

2002

Husmeier, D. (2002) Contribution to: Discussion on the meeting on 'Statistical modelling and analysis of genetic data'. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), p. 751. (doi: 10.1111/1467-9868.00359)

Husmeier, D. and McGuire, G. (2002) Detecting recombination with MCMC. Bioinformatics, 18(Sup 1), S345-S353. (doi: 10.1093/bioinformatics/18.suppl_1.S345)

Husmeier, D. and Wright, F. (2002) A Bayesian approach to discriminate between alternative DNA sequence segmentations. Bioinformatics, 18(2), pp. 226-234. (doi: 10.1093/bioinformatics/18.2.226)

2001

Husmeier, D. and Wright, F. (2001) Detection of recombination in DNA multiple alignments with hidden markov models. Journal of Computational Biology, 8(4), pp. 401-427. (doi: 10.1089/106652701752236214)

Husmeier, D. and Wright, F. (2001) Probabilistic divergence measures for detecting interspecies recombination. Bioinformatics, 17(Sup 1), S123-S131. (doi: 10.1093/bioinformatics/17.suppl_1.S123)

Althoefer, K., Krekelberg, B., Husmeier, D. and Seneviratne, L. (2001) Reinforcement learning in a rule-based navigator for robotic manipulators. Neurocomputing, 37(1-4), pp. 51-70. (doi: 10.1016/S0925-2312(00)00307-6)

Husmeier, D. and Wright, F. (2001) Approximate Bayesian discrimination between alternative DNA mosaic structures. In: Wingender, E., Hofestaedt, R. and Liebich, I. (eds.) Computer Science and Biology: Proceedings of the German Conference on Bioinformatics. German Research Center for Biotechnology: Braunschweig, Germany, pp. 182-184. ISBN 9783000081149

2000

Husmeier, D. (2000) The Bayesian evidence scheme for regularizing probability-density estimating neural networks. Neural Computation, 12(11), pp. 2685-2717. (doi: 10.1162/089976600300014890)

Husmeier, D. (2000) Learning non-stationary conditional probability distributions. Neural Networks, 13(3), pp. 287-290. (doi: 10.1016/S0893-6080(00)00018-6)

Husmeier, D. (2000) Bayesian regularization of hidden Markov models with an application to bioinformatics. Neural Network World, 10(4), pp. 589-595.

Husmeier, D. and Wright, F. (2000) Detecting sporadic recombination in DNA alignments with hidden Markov models. In: Bornberg-Bauer, E., Rost, U., Stoye, J. and Vingron, M. (eds.) GCB 2000: Proceedings of the German Conference on Bioinformatics. Logos Verlag: Berlin, Germany, pp. 19-26. ISBN 9783897224988

Penny, W.D., Husmeier, D. and Roberts, S.J. (2000) The Bayesian paradigm: second generation neural computing. In: Lisboa, P.J.G., Ifeachor, E.C. and Srczepaniak, A.S. (eds.) Artificial Neural Networks in Biomedicine. Series: Perspectives in neural computing. Springer-Verlag: London, UK, pp. 11-23. ISBN 9781852330057 (doi: 10.1007/978-1-4471-0487-2_2)

1999

Husmeier, D. , Penny, W.D. and Roberts, S.J. (1999) An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers. Neural Networks, 12(4-5), pp. 677-705. (doi: 10.1016/S0893-6080(99)00020-9)

Husmeier, D. (1999) Neural Networks for Conditional Probability Estimation. Springer. ISBN 9781852330958 (doi: 10.1007/978-1-4471-0847-4)

Husmeier, D. , Patton, G.S., McClure, M.O., Harris, J.R.W. and Roberts, S.J. (1999) Neural networks for predicting Kaposi's sarcoma. In: IJCNN'99: Proceedings, International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers: New York, NY, USA, pp. 3707-3711. ISBN 9780780355309 (doi: 10.1109/IJCNN.1999.836274)

Husmeier, D. and Roberts, S.J. (1999) Regularisation of RBF-Networks with the Bayesian evidence scheme. In: Proceedings of the 9th International Conference on Artificial Neural Networks. IEEE: Edinburgh, UK, pp. 533-538.

Penny, W.D., Husmeier, D. and Roberts, S.J. (1999) Covariance-based weighting for optimal combination of network predictions. In: Proceedings of the 9th International Conference on Artificial Neural Networks. IEEE: Edinburgh, UK, pp. 826-831.

1998

Roberts, S., Husmeier, D. , Rezek, L. and Penny, W. (1998) Bayesian approaches to Gaussian mixture modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), pp. 1133-1142. (doi: 10.1109/34.730550)

Husmeier, D. and Taylor, J.G. (1998) Neural networks for predicting conditional probability densities: improved training scheme combining EM and RVFL. Neural Networks, 11(1), pp. 89-116. (doi: 10.1016/S0893-6080(97)00089-0)

Husmeier, D. and Althoefer, K. (1998) Modelling conditional probabilities with network committees: how overfitting can be useful. Neural Network World, 8(4), pp. 417-439.

Husmeier, D. , Penny, W.D. and Roberts, S.J. (1998) Empirical evaluation of Bayesian sampling for neural classifiers. In: Niklasson, L., Boden, M. and Ziemke, T. (eds.) Proceedings of the 8th International Conference on Artificial Neural Networks. Series: Perspectives in neural computing. Springer: London, UK, pp. 323-328. ISBN 9783540762638

1997

Husmeier, D. and Taylor, J.G. (1997) Predicting conditional probability densities of stationary stochastic time series. Neural Networks, 10(3), pp. 479-497. (doi: 10.1016/S0893-6080(96)00062-7)

Husmeier, D. , Allen, D. and Taylor, J.G. (1997) A universal approximator network for learning conditional probability densities. In: Ellacott, S.W., Mason, J.C. and Anderson, I.J. (eds.) Mathematics of Neural Networks. Series: Operations research/computer science interfaces series, 8 (8). Springer-Verlag: New York, NY, USA, pp. 198-203. ISBN 9781461377948 (doi: 10.1007/978-1-4615-6099-9_32)

Husmeier, D. and Taylor, J.G. (1997) Modelling conditional probabilities with committees of RVFL networks. In: Gerstner, W., Germond, A., Hasler, M. and Nicoud, J.D. (eds.) Proceedings of the 7th International Conference on Artificial Neural Networks. Series: Lecture notes in computer science (1327). Springer: Berlin, Germany, pp. 1053-1058. ISBN 9783540636311

Husmeier, D. and Taylor, J.G. (1997) Predicting conditional probability densities with the Gaussian mixture - RVFL network. In: Smith, G.D., Steele, N.C. and Albrecht, R.F. (eds.) Artificial Neural Networks and Genetic Algorithms. Series: Springer computer science. Springer: Wien, Germany, pp. 477-481. ISBN 9783211830871

1996

Husmeier, D. and Taylor, J.G. (1996) A neural network approach to predicting noisy time series. In: 3rd Brazilian Symposium on Neural Networks, Recife, Brazil, 1996, pp. 221-226.

1992

Steinhoff, H.J., Schlitter, L., Redhardt, A., Husmeier, D. and Zander, N. (1992) Structural fluctuations and conformational entropy in proteins: entropy balance in an intramolecular reaction in methemoglobin. Biochimica et Biophysica Acta: Proteins and Proteomics, 1121(1-2), pp. 189-198.

Schlitter, J. and Husmeier, D. (1992) System relaxation and thermodynamic integration. Molecular Simulation, 8(3-5), pp. 285-295. (doi: 10.1080/08927029208022483)

This list was generated on Wed Nov 20 16:48:20 2024 GMT.

Number of items: 199.

Articles

Li, W. et al. (2020) Analysis of cardiac amyloidosis progression using model-based markers. Frontiers in Physiology, 11, 324. (doi: 10.3389/fphys.2020.00324) (PMID:32425806) (PMCID:PMC7203577)

Giurghita, D. and Husmeier, D. (2018) Statistical modelling of cell movement. Statistica Neerlandica, 72(3), pp. 265-280. (doi: 10.1111/stan.12140)

Aderhold, A., Husmeier, D. and Grzegorczyk, M. (2017) Approximate Bayesian inference in semi-mechanistic models. Statistics and Computing, 27(4), pp. 1003-1040. (doi: 10.1007/s11222-016-9668-8)

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2016) Fast inference in nonlinear dynamical systems using gradient matching. Proceedings of Machine Learning Research, 48, pp. 1699-1707.

Macdonald, B., Higham, C. and Husmeier, D. (2015) Controversy in mechanistic modelling with Gaussian processes. Proceedings of Machine Learning Research, 37, pp. 1539-1547.

Marbach, D. et al. (2012) Wisdom of crowds for robust gene network inference. Nature Methods, 9(8), pp. 796-804. (doi: 10.1038/nmeth.2016)

Grzegorczyk, M. and Husmeier, D. (2011) Non-homogeneous dynamic Bayesian networks for continuous data. Machine Learning, 83(3), pp. 355-419. (doi: 10.1007/s10994-010-5230-7)

Grzegorcyzk, M., Husmeier, D. and Rahnenführer, J. (2010) Modelling nonstationary gene regulatory processes. Advances in Bioinformatics, 2010, pp. 1-17. (doi: 10.1155/2010/749848)

Husmeier, D. (2003) Reverse engineering of genetic networks with Bayesian networks. Biochemical Society Transactions, 31(6), pp. 1516-1518.

Husmeier, D. and McGuire, G. (2002) Detecting recombination with MCMC. Bioinformatics, 18(Sup 1), S345-S353. (doi: 10.1093/bioinformatics/18.suppl_1.S345)

Husmeier, D. and Wright, F. (2002) A Bayesian approach to discriminate between alternative DNA sequence segmentations. Bioinformatics, 18(2), pp. 226-234. (doi: 10.1093/bioinformatics/18.2.226)

Husmeier, D. and Wright, F. (2001) Probabilistic divergence measures for detecting interspecies recombination. Bioinformatics, 17(Sup 1), S123-S131. (doi: 10.1093/bioinformatics/17.suppl_1.S123)

Husmeier, D. (2000) The Bayesian evidence scheme for regularizing probability-density estimating neural networks. Neural Computation, 12(11), pp. 2685-2717. (doi: 10.1162/089976600300014890)

Husmeier, D. (2000) Learning non-stationary conditional probability distributions. Neural Networks, 13(3), pp. 287-290. (doi: 10.1016/S0893-6080(00)00018-6)

Husmeier, D. (2000) Bayesian regularization of hidden Markov models with an application to bioinformatics. Neural Network World, 10(4), pp. 589-595.

Husmeier, D. and Althoefer, K. (1998) Modelling conditional probabilities with network committees: how overfitting can be useful. Neural Network World, 8(4), pp. 417-439.

Husmeier, D. and Taylor, J.G. (1997) Predicting conditional probability densities of stationary stochastic time series. Neural Networks, 10(3), pp. 479-497. (doi: 10.1016/S0893-6080(96)00062-7)

Schlitter, J. and Husmeier, D. (1992) System relaxation and thermodynamic integration. Molecular Simulation, 8(3-5), pp. 285-295. (doi: 10.1080/08927029208022483)

Books

Husmeier, D. (1999) Neural Networks for Conditional Probability Estimation. Springer. ISBN 9781852330958 (doi: 10.1007/978-1-4471-0847-4)

Book Sections

Research Reports or Papers

Liu, Z., Macdonald, B. , Husmeier, D. and Giurghita, D. (2017) Estimating Parameters of Partial Differential Equations with Gradient Matching. Other. University of Glasgow. (Unpublished)

Conference or Workshop Item

Giurghita, D. and Husmeier, D. (2017) Statistical Modelling of Cell Movement Data Using the Unscented Kalman Filter. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Husmeier, D. , Ferguson, E. , Matthiopoulos, J. and Insall, R. (2017) Statistical Inference of the Drivers of Collective Cell Movement. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Lazarus, A., Husmeier, D. and Papamarkou, T. (2017) Inference in Complex Systems Using Multi-Phase MCMC Sampling with Gradient Matching. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Conference Proceedings

Giurghita, D. and Husmeier, D. (2017) Statistical Modelling of Cell Movement. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 317-322.

Grzegorczyk, M., Aderhold, A. and Husmeier, D. (2015) Network Reconstruction with Realistic Models. In: 30th International Workshop on Statistical Modelling, Linz, Austria, 06-10 Jul 2015,

Noè, U., Filippone, M. and Husmeier, D. (2015) Emulation of ODEs with Gaussian Processes. In: 30th International Workshop on Statistical Modelling, Linz, Austria, 06-10 Jul 2015, pp. 191-194.

Ji, R. and Husmeier, D. (2012) Warped Gaussian process modelling of transcriptional regulation. In: 9th International Workshop on Computational Systems Biology, Ulm, Germany, 4-6 Jun 2012,

Husmeier, D. and Taylor, J.G. (1996) A neural network approach to predicting noisy time series. In: 3rd Brazilian Symposium on Neural Networks, Recife, Brazil, 1996, pp. 221-226.

This list was generated on Wed Nov 20 16:48:20 2024 GMT.

Grants

Ongoing Research Support:

Project title: SofTMechSET – The soft-tissue mechanics statistical emulation and translation hub. Funder: EPSRC. Funder reference: EP/T017899/1. Start: 1 Mach 2021. Duration: 4 years. Role: PI.

Project title: SofTMech with MIT and Polimi. Funder: EPSRC. Funder reference: EP/S030875/1. Start: 01/01/2020. Duration: 4 years. Role: CI.

Project title: MICA: A Developmental Trial of Personalised Medicine for Repurposing Zibotentan, a Selective Endothelin A Receptor Blocker, in Microvascular Angina. Funder: MRC. Funder reference: MR/S018905/1. Start: 01/06/2019. Duration: 40 months. Role: CI.

Project title: A whole-heart model of multiscale soft tissue mechanics and fluid structure interaction for clinical applications (Whole-Heart-FSI). Funder: EPSRC. Funder reference: EP/S020950/1. Start: 01/10/2019. Duration: 5 years. Role: CI.

Project title: Multiscale inference for understanding collective animal movement. Funder: Leverhulme Trust. Funder reference: RPG-2018-398. Start: 01/09/2019. Duration: 42 months. Role: CI.

Project title: Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics. Funder: EPSRC. Funder reference: EP/R018634/1/ Start: 01/05/2018. Duration: 5 years. Role: CI.

Recently Completed Research Support (within the last 5 years):

Project title: EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancer. Funder: EPSRC. Funder reference: EP/N014642/1. Start: 01/04/2016. Duration: 4 years. Role: CI.

Project title: Inference of cardio-mechanical parameters in real time: moving mathematical modelling into the clinic. Funder: Royal Society of Edinburgh. Funder reference: 62335. Start: 01/07/2019. Duration: 1 year. Role: PI.

Project title: Computational inference of biopathway dynamics and structures. Funder: EPSRC. Funder reference: EP/L020319/1. Start: 17/11/2014. Duration: 3 years. Role: PI.

Project title: First steps towards modelling myocardial infarction (a computed MI Physiome): A case-control study of novel biomechanical parameters in acute MI survivors with left ventricular dysfunction. Funder: British Heart Fondation (BHF). Funder reference: PG/14/64/31043. Start: 01/04/2015. Duration: 2 years. Role: CI.

Project title: Understanding Cancer Metastasis by Combining Models, Numbers and Molecules. Funder: Cancer Research UK. Funder reference: C22713/A20017. Start: 01/10/2015. Duration: 4 years. Role: CI.

Project title: TiMet - Linking the Clock to Metabolism. Funder: European Commission FP7, call FP7-KBBE-2009-3. Funder reference: 245143. Start date: 01/03/2010. Duration: 5 years. Role: CI.

Supervision

Current PhD students:
- Nazareno Campioni
- Shaykhah Aldossari
- David Dalton
- Yalei Yang
- Fergus Chadwick
- Yuzhang Ge
- Atrayee Bhattacharya

Current postdocs:
- Alan Lazarus
- Mihaela Paun
- Agnieszka Borowska

Bhattacharya, Atrayee
Predicting patterns of retinal haemorrhage
Ryan, William
Emulation methods to improve inference performance for complex models
Zhang, Jiaming
Physics-Informed Neural Network in Soft Tissue Mechanics

Former PhD students:

- Adriano Werhli
- Wolfgang Lehrach
- Frank Dondelinger
- Alexander Mantzaris
- Andrej Aderhold
- Benn Macdonald
- Vinny Davies
- Elaine Ferguson
- Michela Pasetto
- Umberto Noe
- Diana Giurghita
- Ionut Paun
- Alan Lazarus
- Lucy Cotgrove
- Jon Devlin

Former postdocs:
- Kuang Lin
- Marco Grzegorczyk
- Frank Dondelinger
- Ji Ruirui
- Catherine Higham
- Jennifer Gaskell
- Arash Rabbani

Teaching

I have taught the following courses in the past:

Stochastic Processes

Principles of Probability and Statistics

Module 3 of the SMSTC Statistics course

I currently teach:

Multivariate Methods

Professional activities & recognition

Prizes, awards & distinctions

2019: International Conference of Statistics: Theory and Applications (ICSTA) (Best paper award)
2021: International Conference of Statistics: Theory and Applications (ICSTA) (Best paper award)

Research fellowships

2019 - 2020: Sabbatical grant, Royal Society of Edinburgh

Editorial boards

2013: Statistics and Computing
2010: Statistical Applications in Genetics and Molecular Biology
2011: IEEE/ACM Transactions on Computational Biology and Bioinformatics
2015 - 2018: Journal of the Royal Statistical Society, Series C
2022: SIAM/ASA Journal on Uncertainty Quantification

Selected international presentations

2018: Invited speaker at International Workshop on Parameter Estimation for Biological Models (North Carolina State University)
2018: Invited talk at ECCM (European Conference on Computational Mechanics) ()
2018: Invited talk (Centro Atomico, Bariloche, Argentina)
2018: Invited talk (Laboratorio Ecotono, Bariloche, Argentina)
2015: Invited keynote talk at 12th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (Naples, Italy)

Research datasets

Jump to: 2022 | 2021 | 2017 | 2016

Number of items: 9.

2022

Dalton, D., Gao, H. and Husmeier, D. (2022) Data From: Emulation of Cardiac Mechanics using Graph Neural Networks. [Data Collection]

2021

Torney, C. , Morales, J. and Husmeier, D. (2021) A hierarchical machine learning framework for the analysis of large scale animal movement data. [Data Collection]

2017

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2017) Parameter Inference in Differential Equation Models of Biopathways using Time Warped Gradient Matching. [Data Collection] (Unpublished)

2016

Aderhold, A., Husmeier, D. and Grzegorczyk, M. (2016) Approximate Bayesian inference in semi-mechanistic models. [Data Collection]

Aderhold, A., Husmeier, D. and Grzegorczyk, M. (2016) Hierarchical Bayesian Regression (HBR) and Analytic Gradient Calculation (GCGP). [Data Collection]

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2016) Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching. [Data Collection]

Macdonald, B., Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2016) Approximate parameter inference in systems biology using gradient matching: a comparative evaluation. [Data Collection]

Macdonald, B., Higham, C. and Husmeier, D. (2016) Controversy in mechanistic modelling with Gaussian processes. [Data Collection]

Niu, M., Filippone, M., Husmeier, D. and Rogers, S. (2016) Inference in nonlinear differential equations. [Data Collection]

This list was generated on Thu Nov 21 03:26:31 2024 GMT.

Additional information

I have served on the programme committees of 16 international workshops and conferences. I am a member of the editorial boards of four journals (Statistics and Computing, SIAM/ASA Journal on Uncertainty Quantification, IEEE/ACM Transactions on Computational Biology and Bioinformatics, and Statistical Applications in Genetics and Molecular Biology), and I served as an associate editor of the Journal of the Royal Statistical Society, Series C (Applied Statistics) from 2014 to 2018 (editorial board membership is restricted to 4 years for this journal). I was appointed as external examiner for the Faculty of Mathematics of the University of Cambridge from 2013 to 2016 (Programme: MPhil in Computational Biology), by the National University of Galway from January to June 2018 to act as reviewer for their research assessment exercise, and I am the Director of the EPSRC-funded Research Hub on statistical emulation in soft-tissue mechanics, which started on 1st March 2021.

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Clarity

Professor Dirk Husmeier

Research interests

Research groups

Publications

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1992

Articles

Books

Book Sections

Research Reports or Papers

Conference or Workshop Item

Conference Proceedings

Grants

Supervision

Teaching

Professional activities & recognition

Prizes, awards & distinctions

Research fellowships

Editorial boards

Selected international presentations

Research datasets

2022

2021

2017

2016

Additional information