Dirk Husmeier Vis

*	2009
27	EE	Marco Grzegorczyk, Dirk Husmeier: Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks. PRIB 2009: 113-124
26	EE	Alexander V. Mantzaris, Dirk Husmeier: Distinguishing Regional from Within-Codon Rate Heterogeneity in DNA Sequence Alignments. PRIB 2009: 187-198
25	EE	Iain Milne, Dominik Lindner, Micha Bayer, Dirk Husmeier, Gráinne McGuire, David F. Marshall, Frank Wright: TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops. Bioinformatics 25(1): 126-127 (2009)
	2008
24	EE	Marco Grzegorczyk, Dirk Husmeier, Kieron D. Edwards, Peter Ghazal, Andrew J. Millar: Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler. Bioinformatics 24(18): 2071-2078 (2008)
23	EE	Adriano V. Werhli, Dirk Husmeier: Gene Regulatory Network Reconstruction by Bayesian Integration of Prior Knowledge and/or Different Experimental Conditions. J. Bioinformatics and Computational Biology 6(3): 543-572 (2008)
22	EE	Marco Grzegorczyk, Dirk Husmeier: Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Machine Learning 71(2-3): 265-305 (2008)
	2006
21	EE	Adriano V. Werhli, Marco Grzegorczyk, Dirk Husmeier: Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinformatics 22(20): 2523-2531 (2006)
20	EE	Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams: A regularized discriminative model for the prediction of protein-peptide interactions. Bioinformatics 22(5): 532-540 (2006)
	2005
19	EE	Dirk Husmeier: Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models. ECCB/JBI 2005: 172
18	EE	Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams: Probabilistic in Silico Prediction of Protein-Peptide Interactions. Systems Biology and Regulatory Genomics 2005: 188-197
17	EE	Dirk Husmeier, Frank Wright, Iain Milne: Detecting interspecific recombination with a pruned probabilistic divergence measure. Bioinformatics 21(9): 1797-1806 (2005)
	2004
16	EE	Iain Milne, Frank Wright, Glenn Rowe, David F. Marshall, Dirk Husmeier, Gráinne McGuire: TOPALi: software for automatic identification of recombinant sequences within DNA multiple alignments. Bioinformatics 20(11): 1806-1807 (2004)
	2003
15		Dirk Husmeier: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19(17): 2271-2282 (2003)
	2002
14		Dirk Husmeier, Gráinne McGuire: Detecting recombination with MCMC. ISMB 2002: 345-353
13		Dirk Husmeier, Frank Wright: A Bayesian approach to discriminate between alternative DNA sequence segmentations. Bioinformatics 18(2): 226-234 (2002)
12		Dirk Husmeier, Frank Wright: Detection of Recombination in DNA Multiple Alignments with Hidden Markov Models. Journal of Computational Biology 8(4): 401-427 (2002)
	2001
11		Dirk Husmeier, Frank Wright: Approximate Bayesian Discrimination between Alternative DNA Mosaic Structures. German Conference on Bioinformatics 2001: 182-184
10		Dirk Husmeier, Frank Wright: Probabilistic divergence measures for detecting interspecies recombination. ISMB (Supplement of Bioinformatics) 2001: 123-131
9	EE	Kaspar Althoefer, Bart Krekelberg, Dirk Husmeier, Lakmal D. Seneviratne: Reinforcement learning in a rule-based navigator for robotic manipulators. Neurocomputing 37(1-4): 51-70 (2001)
	2000
8		Dirk Husmeier, Frank Wright: Detecting Sporadic Recombination in DNA Alignments with Hidden Markov Models. German Conference on Bioinformatics 2000: 19-26
7		Dirk Husmeier: The Bayesian Evidence Scheme for Regularizing Probability-Density Estimating Neural Networks. Neural Computation 12(11): 2685-2717 (2000)
6	EE	Dirk Husmeier: Learning non-stationary conditional probability distributions. Neural Networks 13(3): 287-290 (2000)
	1999
5	EE	Dirk Husmeier, William D. Penny, Stephen J. Roberts: An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers. Neural Networks 12(4-5): 677-705 (1999)
	1998
4	EE	Stephen J. Roberts, Dirk Husmeier, Iead Rezek, William D. Penny: Bayesian Approaches to Gaussian Mixture Modeling. IEEE Trans. Pattern Anal. Mach. Intell. 20(11): 1133-1142 (1998)
3	EE	Dirk Husmeier, John G. Taylor: Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL. Neural Networks 11(1): 89-116 (1998)
	1997
2		Dirk Husmeier, John G. Taylor: Modeling Conditional Probabilities with Committees of RVFL Networks. ICANN 1997: 1053-1058
1	EE	Dirk Husmeier, John G. Taylor: Predicting Conditional Probability Densities of Stationary Stochastic Time Series. Neural Networks 10(3): 479-497 (1997)

Coauthor Index

1	Kaspar Althoefer	[9]
2	Micha Bayer (M. M. Bayer)	[25]
3	Kieron D. Edwards	[24]
4	Peter Ghazal	[24]
5	Marco Grzegorczyk	[21] [22] [24] [27]
6	Bart Krekelberg	[9]
7	Wolfgang P. Lehrach	[18] [20]
8	Dominik Lindner	[25]
9	Alexander V. Mantzaris	[26]
10	David F. Marshall	[16] [25]
11	Gráinne McGuire	[14] [16] [25]
12	Andrew J. Millar	[24]
13	Iain Milne	[16] [17] [25]
14	William D. Penny	[4] [5]
15	Iead Rezek	[4]
16	Stephen J. Roberts	[4] [5]
17	Glenn Rowe	[16]
18	Lakmal D. Seneviratne	[9]
19	John G. Taylor	[1] [2] [3]
20	Adriano V. Werhli	[21] [23]
21	Christopher K. I. Williams	[18] [20]
22	Frank Wright	[8] [10] [11] [12] [13] [16] [17] [25]

Colors in the list of coauthors