Current Projects

IWT 040571 BIO-IT

Description:
BioScope-IT is FlandersBio's Bioinformatics Service Project to help lower the threshold for Flemish biotech companies to apply bioinformatics. BioScope-IT's main goal is to stimulate bioinformatics awareness by building a solid network of excellence, and help biotech companies to take the first step towards adoption of more advanced computational solutions.
The project aims at dispersing bioinformatics information with pro-active actions (meetings, workshops, seminars,...) on the one hand, and to give site/company-specific technological advice on the other. Biotech companies can direct their bioinformatics related questions towards the BioScope-IT advisor-coordinators, who perform the problem assessment and formulate ad hoc advice.
The project is funded by the framework of the Vlaamse Innovatie Samenwerkingsverbanden (VIS) from the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT).

FWO G.0229.03

Description:
This project aims at the development of an ontology-based knowledge system containing biological information on yeast and prokaryotes with a main emphasis on Salmonellae. Such a knowledge system would allow biologists and bioinformaticians to efficiently retrieve biological information and, most importantly, to use this knowledge in the analysis of microarray data.

FWO G.0388.03

Description:
We will use microarrays to measure expression levels of a given set of genes in ovarian tumor cells (ovarian tumors taken from tissue collections). This gives, for each experiment, an expression vector with thousands of components (1 component for each probe or gene present on the array). The determination of these expression levels can be repeated for ovarian tumor cells coming from different classes or diagnostic categories. We will use data mining techniques (existing and newly developed) to extract medical and biological knowledge from the expression vectors. Basically, this can be divided into three categories:

• Clinical predictions - Class prediction
• Looking for diagnostic categories - Class discovery by cluster analysis
• Gene or feature extraction

GBOU-SQUAD

Description:
SQUAD: Salmonella's quorum sensing to attach disease

CAGE

Description:
The CAGE project aims to build a gene expression reference database. A Consortium of European Arabidopsis functional genomics centers has teamed up with bioinformatics partners that contribute expertise in microarray data processing, analysis and storage/distribution. A total of 2000 Arabidopsis samples will be produced and analysed under largely standardised conditions. These samples will be profiled on CATMA microarrays containing gene specific probes for most Arabidopsis genes, to build a Compendium of expression profiles. The data will be assessed for statistical significance and submitted to the ArrayExpress database at the European Bioinformatics Institute (EBI). EBI will deliver specific CAGE ontology, and data submission pipelines. The Compendium data will be annotated and analysed for content and confirmation of gene function. The Compendium will further be maintained by EBI.

Past Projects

STWW-genprom

Description:
The goal of this project is to apply state-of-the-art pattern recognition techniques to build a prediction engine that finds promoters and regulatory elements in the genome of A. thaliana and other plants. Probabilistic sequence models are used to detect over-represented motifs in groups of co-expressed genes.
The regulatory elements that are known in plants based on literature and biolgical experiments, are stored in a specialised database PlantCARE.

Interdisciplinary Research Project (IDO)

Description:
An interdisciplinary IDO project was set up to develop a generic method applicable for inference of both prokaryotic and eukaryotic regulatory networks based on gene expression data from microarray or RT-PCR experiments using dynamical Bayesian networks.

FWO G.0115.01

Description:
It is assumed that most cancers originate from genetic disorders. A more profound insight in these mechanisms will unmistakably be of major importance in making the right predictions and decisions. The goal of this project is to apply advanced datamining methods on microarray experiments for oncology.
The phenotype of the tumor is determined by the collection of disturbed gene expression levels. Microarrays allow us to measure the expression levels of thousands of genes simultaneously. These microarray experiments are repeated for different samples, under different conditions (e.g., tumor cells before and after metastasis, tumor cells originating from different tumor types, etc...). Comparing expression levels of genes/samples (by the use of data mining methods) allows us to perform:

1. Class discovery (e.g., to determine the different tumor types or to group genes with similar behaviour)
2. Class prediction (e.g., to predict diagnosis and staging)
3. Feature selection (e.g. to select the relevant genes).

FWO G.0413.03

Description: