Dirk Husmeier - Publications

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After joining Glasgow University

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

Macdonald B., Dondelinger F. and Husmeier D. (2013)
Inference in complex biological systems with Gaussian processes and parallel tempering
Proceedings of the 28th International Workshop on Statistical Modelling, Vol. 2, 673-676.
ISBN 978-88-96251-49-2

Davies V. and Husmeier D. (2013)
Assessing the impact of non-additive noise on modelling transcriptional regulation with Gaussian processes
Proceedings of the 28th International Workshop on Statistical Modelling, Vol. 2, 559-562.
ISBN 978-88-96251-49-2

Higham C.F. and Husmeier D. (2013)
A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana
BMC Bioinformatics 14 (Suppl 10):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, Volume 91, Issue 1, pp 105-154.

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.
Proceedings of the 10th International Workshop on Computational Systems Biology, 29-33. ISBN 978-952-15-3091-3. ISSN 1456-2774.

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

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 (JMLR-WCP) 31: 75–84.

Dondelinger F., Lebre S., and Husmeier D. (2012)
Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure
Machine Learning, Volume 90, Issue 2, pp 191-230

Marbach, Costello, Kuffner, Vega, Prill, Camacho, Allison, Aderhold, Allison, Bonneau, Camacho, Chen, Collins, Cordero, Costello, Crane, Dondelinger, Drton, Esposito, Foygel, Fuente, Gertheiss, Geurts, Greenfield, Grzegorczyk, Haury, Holmes, Hothorn, Husmeier, Huynh-Thu, Irrthum, Kellis, Karlebach, Kffner, Lebre, Leo, Madar, Mani, Mordelet, Ostrer, Ouyang, Pandya, Petri, Pinna, Poultney, Prill, Rezny, Ruskin, Saeys, Shamir, Sirbu, Song, Soranzo,Statnikov, Vega, Vera-Licona, Vert, Visconti, Wang, Wehenkel, Windhager, Zhang, Zimmer, Kellis, Collins, Stolovitzky (2012)
Wisdom of crowds for robust gene network inference.
Nature Methods 9, 796-804.

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, volume 11, issue 4.

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

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), 361-377

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, 467-476

Ji R. and Husmeier D. (2012)
Warped Gaussian Process Modelling of Transcriptional Regulation
In Larjo A., Schober S., Farhan M., Bossert M. and Yli-Harja O. (eds.):
Proceedings of the Ninth International Workshop on Computational Systems Biology,
Tampere International Center for Signal Processing (ISBN 978-952-15-2853-8), pages 48-51.

Dondelinger F., Rogers S., Filipppone 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 Larjo A., Schober S., Farhan M., Bossert M. and Yli-Harja O. (eds.):
Proceedings of the Ninth International Workshop on Computational Systems Biology,
Tampere International Center for Signal Processing (ISBN 978-952-15-2853-8), pages 16-19.

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 - Methods and Protocols,
Humana Press (ISBN 978-1-61779-399-8), pages 199-214.

Husmeier D. , Werhli A., and Grzegorczyk M. (2011)
Advanced Applications of Bayesian Networks in Systems Biology.
In Stumpf M., Balding D.J. and Girolami M. (eds.):
Handbook of Statistical Systems Biology, John Wiley & Sons (ISBN 978-0-470-71086-9), Chapter 13, pages 270-289.

Books

Husmeier D. , Dybowski R., and Roberts S. (2005)
Probabilistic Modeling in Bioinformatics and Medical Informatics
Advanced Information and Knowledge Processing
Springer Verlag, New York
Review of the book

Husmeier D. (1999)
Neural Networks for Conditional Probability Estimation
Perspectives in Neural Computation
Springer Verlag, London, ISBN 978-1-85233-095-8
Online version

Journals

Grzegorczyk M. and Husmeier D. (2011)
Non-homogeneous dynamic Bayesian networks for continuous data.
Machine Learning 83 (3), 355-419.

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), 693–699.

Grzegorczyk M., Husmeier D. and Rahnenführer J. (2011)
Modelling non-stationary dynamic gene regulatory processes with the BGM model
Computational Statistics 26 (2), 199-218.

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), 451-464.

Grzegorczyk M., Husmeier D. and Rahnenführer J. (2010)
Modelling Non-Stationary Gene Regulatory Processes
Advances in Bioinformatics, Volume 2010, Article ID 749848, doi:10.1155/2010/749848

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 , Article ID 601068, doi:10.1155/2009/601068

Lehrach W.P. and Husmeier D. (2009)
Segmenting bacterial and viral DNA sequence alignments with a trans-dimensional phylogenetic factorial hidden Markov model
Applied Statistics 58 (3), 307-327

Husmeier D. and Mantzaris, A.V. (2008)
Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments
Statistical Applications in Genetics and Molecular Biology , Vol. 7 : Iss. 1, Article 34.

Milne I., Lindner D., Bayer M., Husmeier D., McGuire G., Marshall D.F., and Wright F. (2008)
TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops
Bioinformatics , 25(1):126-127.

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

Werhli A.V. 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

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), 265-305.

Armstrong M.R., Husmeier D. , Phillips M.S. and Bloks 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, 689-701.

Werhli A.V. 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 , Vol. 6 : Iss. 1, Article 15.

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), Article 27.

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): 2523-2531.
Abstract ; Full paper

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

Husmeier D. (2005)
Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models
Abstract , Full text
Bioinformatics 21: ii166-ii172.

Husmeier D. , Wright F., Milne I. (2005)
Detecting interspecific recombination with a pruned probabilistic divergence measure
Bioinformatics 21(9):1797-1806

Milne I., Wright F., Rowe G., Marshall D.F., Husmeier D. , McGuire G. (2004)
"TOPALi: software for automatic identification of recombinant sequences within DNA multiple alignments"
Bioinformatics 20: 1806-1807

Husmeier D. (2003)
"Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks"
Bioinformatics 19: 2271-2282.

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

Husmeier D. , 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):315-337.

Husmeier D. , McGuire G. (2002):
"Detecting recombination with MCMC" ,
Bioinformatics 18: S345-S353. Also presented at ISMB 2002 .

Husmeier D. , Wright F. (2002):
"A Bayesian Approach to Discriminate between Alternative DNA Sequence Segmentations" ,
Bioinformatics 18 (2), 226-234.

Husmeier D. , Wright F. (2001):
"Detection of Recombination in DNA Multiple Alignments with Hidden Markov Models" ,
Journal of Computational Biology 8 (4), 401-427.
Abstract

Husmeier D. , Wright F. (2001):
"Probabilistic Divergence Measures for Detecting Interspecies Recombination" ,
Bioinformatics 17, Suppl. 1, S123-S131. Also presented at ISMB 2001 .

Althoefer K., Krekelberg B., Husmeier D. , Seneviratne L. (2001):
"Reinforcement learning in a rule-based navigator for robotic manipulators" ,
Neurocomputing 37 (1-4), 51-70.

Husmeier D. (2000):
"The Bayesian Evidence Scheme for Regularising Probability-Density Estimating Neural Networks" ,
Neural Computation 12 (11), 2685-2717.

Husmeier D. (2000):
Learning Non-Stationary Conditional Probability Distributions,
Neural Networks 13, 287-290.

Husmeier D. , Penny W., Roberts S.J. (1999):
"An Empirical Evaluation of Bayesian Sampling with Hybrid Monte Carlo for Training Neural Network Classifiers"
Neural Networks 12, 677-705.

Roberts S.J., Husmeier D. , Rezek I., Penny W. (1998):
Bayesian Approaches to Gaussian Mixture Modeling,
IEEE Transactions on Pattern Analysis and Machine Learning 20 (11), 1133-1142.

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

Husmeier D. , Taylor J.G. (1998):
Neural Network for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL,
Neural Networks 11 (1), 89-116.

Husmeier D. , Taylor J.G. (1997):
Predicting Conditional Probability Densities of Stationary Stochastic Time Series,
Neural Networks 10 (3), 479-497.

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

Schlitter J., Husmeier D. (1992):
System Relaxation and Thermodynamic Integration,
Molecular Simulation 8, 285-295.

Book Chapters and Refereed Conference Proceedings

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., Munoz F. (eds): Proceedings of the 26th International Workshop on Statistical Modelling (IWSM). Copiformes S.L., Valencia, July 2011. ISBN 978-84-694-5129-8. Pages 189-194.

Husmeier D., Dondelinger F., and Lebre S. (2010)
Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks
In Lafferty, J et al.: Proceedings of the twenty-fourth annual conference on Neural Information Processing Systems (NIPS). Curran Associates, ISBN 9781617823800, pages 901-909

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 Fenyö D. (ed.): Computational Biology, Springer, Series Methods in Molecular Biology, Volume 673, pages 253-281, ISBN 978-1-60761-841-6.

Dondelinger F., Lebre S., and Husmeier D. (2010)
Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing
Proceedings of the International Conference on Machine Learning (ICML), Eds. Furnkranz J., Joachims T., 303-310. Madison, Wisconsin, USA. ISBN 978-1-60558-907-7.

Lin K. and Husmeier D. (2010)
Mixtures of factor analyzers for modeling transcriptional regulation
in Lawrence, Girolami, Rattray and Sanguinetti (eds.): Learning and Inference in Computational Systems Biology, MIT press, Cambridge, MA, ISBN 978 0 262 01386 4, pages 153-200.

Grzegorczyk M. and Husmeier D. (2009)
Non-stationary continuous dynamic Bayesian networks
In Bengio, Schuurmans, Lafferty, Williams and Culotta (eds.): Proceedings of the twenty-third annual conference on Neural Information Processing Systems (NIPS 2009), Curran Associates, ISBN 9781605603520, pages 682--690

Grzegorczyk M. and Husmeier D. (2009)
Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks
In: V. Kadirkamanathan et al. (Eds.): Pattern Recognition in Bioinformatics
Lecture Notes in Bioinformatics, Springer-Verlag Berlin Heidelberg, ISBN 978-3-642-04030-6, pp. 113-124

Mantzaris A.V. and Husmeier D. (2009)
Distinguishing Regional from Within-Codon Rate Heterogeneity in DNA Sequence Alignments
In: V. Kadirkamanathan et al. (Eds.): Pattern Recognition in Bioinformatics
Lecture Notes in Bioinformatics, Springer-Verlag Berlin Heidelberg, ISBN 978-3-642-04030-6, pp. 187-198
Winner of the best student paper award

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: T. Manninen et al. (Eds.): Proceedings of the Sixth International Workshop on Computational Systems Biology, WCSB 2009, Aarhus, Denmark, pp. 51-54, ISBN 978-952-15-2160-7

Grzegorczyk M., Husmeier D. and Werhli A.V. (2008)
Reverse Engineering Gene Regulatory Networks with Various Machine Learning Methods
In: Emmert-Streib F. and Dehmer M. (editors)
Analysis of Microarray Data: A Network-Based Approach
Wiley-VCH, Weinheim, 2008, pages 101-142.

Husmeier D. and Werhli A.V. (2007):
"Bayesian Integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks" .
In Xu Y. and Markstein P. (eds.): Proceedings of the International Conference on Computational Systems Bioinformatics (CSB 2007), Vol. 6, p. 85-95
Imperial College Press, ISSN 1752-7791, ISBN 978-1-86094-872-5

Lehrach W.P., Husmeier D. and Williams C.K.I. (2006)
Probabilistic in silico prediction of protein-peptide interactions
In: Eskin E., Ideker T., Raphael B. and Workman C. (editors)
Systems Biology and Regulatory Genomics
Springer Verlag, San Diego, 978-3-540-48293-2, pages 188-197

Husmeier D. (2006)
Detecting Mosaic Structures in DNA Sequence Alignments
In: Misra JC (editor)
Biomathematics: Modelling and Simulation
World Scientific, ISBN 981-238-110-4

Werhli A.V., 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. (editors)
Statistics in Genomics and Proteomics
Centro Internacional de Matematica, Coimbra, Portugal, ISBN: 989-95011-0-7, pages 23-34.

Husmeier D. , Wright F. (2001):
"Approximate Bayesian Discrimination between Alternative DNA Mosaic Structures" .
In Wingender E., Hofestaedt R., Liebich I. (eds.): 16th German Conference on Bioinformatics (GCB 2001). ISBN 3-00-008114-3, pages 182-184.

Husmeier D. , Wright F. (2000):
"Detecting Sporadic Recombination in DNA Alignments with Hidden Markov Models" .
In Bornberg-Bauer E., Rost U., Stoye J., Vingron M. (eds.): 15th German Conference on Bioinformatics (GCB 2000) , Logos Verlag Berlin (ISBN 3-89722-498-4), 19-26.

Husmeier D. (2000):
"Bayesian Regularization of Hidden Markov Models with an Application to Bioinformatics",
International Conference on Artificial Neural Networks and Intelligent Systems ,Prague, Czech Republic, July 9-12, 2000. Published in Neural Network World 10 (4), 589-595.

Penny W.D., Husmeier D. , Roberts S.J. (1999):
"The Bayesian Paradigm: Second Generation Neural Computing."
In: Lisboa P.J.G., Ifeachor E.C., Srczepaniak A.S. (Ed.), Artificial Neural Networks in Biomedicine, Springer (ISBN: 1-85233-005-8), 11-23.

Husmeier D. , Roberts S.J. (1999):
"Regularisation of RBF-Networks with the Bayesian Evidence Scheme".
International Conference on Artificial Neural Networks (ICANN99) , IEE Press, Edinburgh, 533-538.

Penny W.D., Husmeier D. , Roberts S.J. (1999):
"Covariance-based weighting for optimal combination of network predictions".
International Conference on Artificial Neural Networks (ICANN99) IEE Press, Edinburgh, 826-831.

Husmeier D. , Patton G.S., McClure M.O., Harris J.R.W., Roberts S.J.(1999):
"Neural Networks for Predicting Kaposi's Sarcoma" .
International Joint Conference on Neural Networks (IJCNN99), 3707-3711.
ISBN 0-7803-5529-6. DOI: 10.1109/IJCNN.1999.836274.

Husmeier D. , Penny W.D., Roberts S.J. (1998):
"Empirical Evaluation of Bayesian Sampling for Neural Classifiers" .
In: Niklasson L., Boden M., Ziemke T. (eds.): International Conference on Artificial Neural Networks - ICANN '98 , Perspectives in Neural Computing, Springer Verlag (ISBN 3-540-76263-9), 323-328.

Husmeier D. , Taylor J.G. (1997):
" Modelling Conditional Probabilities with Committees of RVFL Networks "
In: Gerstner W., Germond A., Hasler M., Nicoud J.D. (eds.): International Conference on Artificial Neural Networks - ICANN '97 , Lecture Notes in Computer Science 1327, Springer Verlag (ISBN 3-540-63631-5), 1053-1058.

Husmeier D. , Taylor J.G. (1997):
"Predicting Conditional Probability Densities with the Gaussian Mixture - RVFL Network."
In:  Smith G.D.,  Steele N.C.,  Albrecht R.F. (Eds.): Artificial Neural Networks and Genetic Algorithms , 477-481,  Springer Verlag, ISBN  3-211-83087-1.

Husmeier D. , Allen D., Taylor J.G. (1997):
A Universal Approximator for Learning Conditional Probability Densities,
in Ellacott S.W., Mason J.C., Anderson I.J. (eds.): Mathematics of Neural Networks: Models, Algorithms, and Applications , Kluwer Academic Press, Boston (ISBN: 0-7923-9933-1), 198-203.

Husmeier D. , Taylor J.G. (1996):
"A Neural Network Approach to Predicting Noisy Time Series",
in Ludermir T.B. (ed.): Annals of the Third Brazilian Symposium on Neural Networks, 221-226, Recife 1996.

Conference Posters

Lin K. and Husmeier D. (2008)
Modelling transcriptional regulation with a variational Bayesian mixture of factor analysers
MASAMB 08 , Glasgow, UK, 27-28 March 2008

DeKoning D.J., Aitman T.J., Druka A., Waugh R., Whittaker J., Husmeier D. , Rawlings C., and Haley, C.S. (2008)
GENESYS: The exploitation of genetic variation in gene network inference
MASAMB 08 , Glasgow, UK, 27-28 March 2008

Lin K. and Husmeier D. (2007)
Modelling Transcriptional Regulation with a Bayesian Mixture of Factor Analysers and Variational Learning Methods
CSB 07 , San Diego, CA, 13-17 August 2007

Werhli A.V., Grzegorczyk M., Husmeier D. and Urfer W. (2006)
Comparative Evaluation of the Accuracy of Reverse Engineering Gene Regulatory Networks with various Machine Learning Methods
ISMB 06 , Fortaleza, Brazil

Grzegorczyk M., Werhli A.V., Urfer W. and Husmeier D. (2006)
Reverse engineering protein regulatory networks using graphical models.
German Conference on Bioinformatics , Tubingen, Germany, 20-22 September 2006

Lehrach W., Husmeier D. , Williams C. (2005)
A Regularised Discriminative Model for the Prediction of Protein-Peptide Interactions
ECCB 2005 , Madrid, Spain

Husmeier D. (2005)
Phylogenetic Factorial Hidden Markov Models for Detecting Mosaic Structures in DNA Sequence Alignments
ISMB 2005 , Detriot, USA

Husmeier D. , Wright F., Milne I. (2004)
Pruning the probabilistic divergence measure for improved detection of interspecific recombination
ISMB 2004 , Glasgow, UK

Lehrach W., Husmeier D. , Williams C., Barber D. (2004)
Using TDNNs to Predict Protein Interactions by Locating Relevant Sequence Features
ISMB 2004 , Glasgow, UK

Husmeier D. (2004)
Pruned PDM Method for Detecting Recombination
In Apostol Granada and Philip E. Bourne (eds.): Currents in Computational Molecular Biology , poster proceedings of RECOMB 2004 , pp.264-265.

Husmeier D. (2003)
Inferrring gene interactions with Bayesian networks
In Spang R., Beziat P., Vingron M. (eds.): Currents in Computational Molecular Biology , poster proceedings of RECOMB 2003 , pp.303-304.

Paper discussions

Husmeier D. (2011)
Contribution to the discussion on the paper by Girolami and Calderhead
"Riemann manifold Langevin and Hamiltonian Monte Carlo methods"
Journal of the Royal Statistical Society B , 73 (2), 184-185

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 A , 170 (4), 340

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 B , 66 (4), 840-841

Husmeier D. (2002)
Contribution to the discussion on statistical modelling and analysis of genetic data
Journal of the Royal Statistical Society B , 64 (4), 751

Technical reports

Husmeier D. , McGuire G. (2002):
Detecting Recombination in DNA Sequence Alignments: A Comparison between BARCE and RECPARS

Husmeier D. , Wright F. (2001):
Detecting past recombination events in Potato virus Y genomic sequences using statistical methods
Scottish Crop Research Institute, Annual Report 2000/2001 , 158-162.
ISBN 0 9058 75176

Theses and Dissertations

Husmeier D. (2011):
Probabilistic Models for Molecular Phylogenetics and Systems Biology
Habilitationsschrift (thesis for a higher doctorate), Department of Statistics, University of Dortmund, 2013, 146 pages.

Husmeier D. (1997):
"Modelling Conditional Probability Densities with Neural Networks",
PhD thesis , King's College, London 1997, 307 pages.

Husmeier D. (1994):
Time Series Prediction with Neural Networks.
MSc dissertation , Department of Mathematics, King's College London, 55 pages.

Husmeier D. (1991):
Numerische Bestimmung des Loesungsmitteleinflusses auf die thermodynamischen Groessen einer intramolekularen Proteinreaktion. (In German. English translation: Numerical estimation of the influence of the solvent on the thermodynamic entities in an intramolecular protein reaction.)
Diplomarbeit , Department of Biophysics, University of Bochum, 343 pages.