Professor Dirk Husmeier

  • Chair of Statistics (Statistics)

telephone: 5141
email: Dirk.Husmeier@glasgow.ac.uk

Mathematics & Statistics
Room 221


Personal website

Research Interests

My research focuses on the development of novel statistical and machine learning methods for bioinformatics and computational biology, with an emphasis on Bayesian inference. My recent research projects were related to molecular phylogenetics, pattern recognition in DNA sequence alignments, the detection of intraspecific recombination in bacteria and viruses, the reconstruction of gene regulatory networks from transcriptomic profiles and postgenomic data integration, and the development of improved MCMC samplers for Bayesian learning of Bayesian networks. My current research focuses on improved Bayesian hierarchical models for the prediction of molecular regulatory networks subject to adaptation, the inference of species interaction networks in ecology, and I'm just about to have a first go at Bayesian inference in mechanistic models of molecular pathways.

Research Groups

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Number of items: 92.

2015

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, (doi:10.1515/sagmb-2014-0041) (Early Online Publication)

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. Journal of Machine Learning Research: Workshop and Conference Proceedings, 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. Journal of Machine Learning Research: Workshop and Conference Proceedings, 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. In: Sixteenth International Conference on Artificial Intelligence and Statistics, Scottsdale, AZ, USA, 29 Apr - 1 May 2013,

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. Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings, 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), 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 Mon May 4 06:51:25 2015 BST.
Number of items: 92.

Articles

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, (doi:10.1515/sagmb-2014-0041) (Early Online Publication)

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. Journal of Machine Learning Research: Workshop and Conference Proceedings, 33, pp. 149-158.

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. Journal of Machine Learning Research: Workshop and Conference Proceedings, 31, pp. 75-84.

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.

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)

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

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)

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)

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)

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)

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)

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), 543-572 . (doi:10.1142/S0219720008003539)

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.

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)

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.

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)

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)

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., 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)

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)

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.

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)

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. (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., 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)

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., 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)

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)

Books

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

Book Sections

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

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

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

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)

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., 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)

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

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)

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

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 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

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)

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

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

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)

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.

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

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

Conference Proceedings

Dondelinger, F., Filippone, M., Rogers, S., and Husmeier, D. (2013) ODE parameter inference using adaptive gradient matching with Gaussian processes. In: Sixteenth International Conference on Artificial Intelligence and Statistics, Scottsdale, AZ, USA, 29 Apr - 1 May 2013,

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,

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 Mon May 4 06:51:25 2015 BST.

EPSRC Bridge the Gap January - June 2012

EU FP7 project Timet

Current research staff

Catherine Higham
Mu Niu

Current PhD students

Andreij Aderhold (U of St Andrews)
Vinny Davies (Bayesian Computational Statistics in Systems Biology )
Diana Giurghita (Unifying principles of migration at multiple scales: linking cancer research to ecology)
Benn Macdonald (Parameter inference in mechanistic models of biological pathways with applications in biomedicine )
Umberto Noe (Bayesian non-­parametric inference in mechanistic models of complex biological systems)

Principles of Probability and Statsitics (POPS)

Stochastic Processes


Module 3 of the SMSTC Statistics stream