A wealth of genomic data has become available since the introduction of microarray technology (Figure 1). The different types of experiments each provide complementary information about the biological system of interest. Because each of these data sources provide only limited insight in the biological system, there is a huge interest in machine learning methods that integrate two or more of these different data sources. However, since researchers do not know the real biological system, validation of their machine learning method is often difficult or even impossible for some aspects of the method.
The goal of this thesis is to extend an existing model of the inner working of a cell, called
SynTReN [1], that generates synthetic gene networks and corresponding gene expression data. Possible extensions to the current model include generating protein binding data and generating synthetic DNA sequences. The synthetic DNA sequences can for example be modelled using Hidden Markov Models.
The thesis consists of two major parts. First the existing model will be extended, including updating the user interface to reflect the new extensions. Second, you will apply the generated data to one or more integrative machine learning methods and assess how well these methods work under different parameter settings. One of these integrative machine learning methods, called Genomica [2], is based on Probabilistic Relational Models (a relational extension to Bayesian networks).
Thesis students can expect to gain insight in the latest developments in probabilistic models and systems biology.
Links:

[1] SynTReN: http://homes.esat.kuleuven.be/~kmarchal/SynTReN/index.html

[2] Genomica: http://genomica.weizmann.ac.il/
Profile :

Broad interest in both machine learning and biology

Programming experience (Java preferably)
Number of students: 2 or 1
Work:

Literature: 30%, Exploration: 30%, Implementation: 40%
Contacts :
Tutors: Tim Van den Bulcke ( tim.vandenbulcke@esat.kuleuven.be
)
Wouter Van Delm ( wouter.vandelm@esat.kuleuven.be
)
Co-promotor: Kathleen Marchal ( kathleen.marchal@biw.kuleuven.be
)
Promotor: Bart De Moor ( bart.demoor@esat.kuleuven.be
)