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Markus Brameier

Bioinformatics Research Center (BiRC)
University of Aarhus
DK-8000 Aarhus C
Denmark


Research Interests

Bioinformatics:	Systems Biology, Functional Genomics and Proteomics, Biological Networks
Biomedical Informatics:	MicroRNA, Cancer Research, Microarray Data Analysis, SNP Arrays
Evolutionary Algorithms:	Genetic Programming, Linear Genetic Programming
Machine Learning:	Neural Networks, Self-Organizing Maps

Selected Publications

M. Brameier,
Neural Networks in Data-mining and Knowledge Discovery.
In R.A. Meyers (ed.) Encyclopedia of Complexity and Systems Science, Springer, Berlin, 2009.



M. Brameier and W. Banzhaf,
Linear Genetic Programming.
Springer, New York, 2007.

M. Brameier and C. Wiuf,
Ab Initio Identification of Human MicroRNAs based on Structure Motifs.
BMC Bioinformatics, 8:478, 2007.

M. Brameier, A. Krings, and R.M. MacCallum,
NucPred - Predicting Nuclear Localization of Proteins.
Bioinformatics, 23(9), 2007.

M. Brameier and C. Wiuf,
Co-Clustering and Visualization of Gene Expression Data and Gene Ontology Terms for Saccharomyces Cerevisiae using Self-Organizing Maps.
Journal of Biomedical Informatics, 40(2):160-173, 2007 (online 2006).

C. Wiuf, M. Brameier, O. Hagberg, and M.P.H. Stumpf,
A Likelihood Approach to Analysis of Network Data.
PNAS, 103:7566-7570, 2006.

M. Brameier, J. Haan, A. Krings, and R.M. MacCallum,
Automatic Discovery of Cross-Family Sequence Features Associated with Protein Function.
BMC Bioinformatics, 7:16, 2006.

A. Heddad, M. Brameier, and R.M. MacCallum,
Evolving Regular Expression-based Sequence Classifiers for Protein Nuclear Localisation.
In Proceedings of the Second European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2004), LNCS, vol. 3005, pp 31-40, Springer, Berlin, 2004.

M. Brameier and W. Banzhaf,
Neutral Variations Cause Bloat in Linear GP.
In Proceedings of the Sixth European Conference on Genetic Programming (EuroGP 2003), LNCS, vol. 2610, pp. 286-296, Springer, Berlin, 2003.
Best poster paper award

W. Banzhaf, M. Brameier,  M. Stautner, and K. Weinert,
Genetic Programming and its Application in Machining Technology.
In H.-P. Schwefel, I. Wegener, and K. Weinert (eds.) Advances in Computational Intelligence - Theory and Practice, Springer, Berlin, 2002.
(including a section about linear GP [preprint])

M. Brameier and W. Banzhaf,
Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming.
In Proceedings of the Fifth European Conference on Genetic Programming (EuroGP 2002), LNCS, vol. 2278, pp. 37-49, Springer, Berlin, 2002.
Best paper award

M. Brameier and W. Banzhaf,
Evolving Teams of Predictors with Linear Genetic Programming.
Genetic Programming and Evolvable Machines, 2(4):381-407, 2001.

M. Brameier and W. Banzhaf,
A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining.
IEEE Transactions on Evolutionary Computation, 5(1):17-26, 2001.