- bioanalytical & biomedical pattern discovery and artificial intelligence
- biomedical decision support
- bioinformatics, molecular networks
- [gen-/transcript-/prote-/epigen-/metabol-]omics data integration
- PhD students:
- Former PhD students:
- Thanh Hai Dang (defended in 2012)
- Trungh Nghia Vu (defended in 2014)
- Stefan Naulaerts (defended in 2016)
- Wout Bittremieux (defended in 2017)
- Bart Cuypers (defended in 2018)
- Aida Mrzić (defended in 2018)
- Visiting researchers:
I am a bioinformatics researcher at the Biomedical informatics research center (biomina) and the Advanced database Research and Modelling (ADReM) group at the University of Antwerp. My young but growing biodata mining research team has the ambition to remove crucial limitations in the interpretation of big molecular (such as genome, proteome and metabolome) data by introducing cutting-edge data mining technology.
My two core research areas are:
- biomolecular pattern discovery: we develop, study and apply approaches to discover patterns in large scale molecular data, often with a focus on the interpretation of unexplained information (e.g. originating from mass spectrometry proteome or metabolome analyses);
- from interaction model to biological network: we develop, study and apply methods to model interactions between biomolecules, and reconstruct large scale models of molecular systems, targetting a variety of biomedical and biotechnological applications.
Both biological sciences and clinical medicine are currently overwhelmed by vast amounts of complex data and are becoming increasingly dependent on information technology for data analysis, interpretation and organisation. Although powerful data mining techniques are nowadays being developed, they are still underutilized in the life sciences. Biomina (biomedical informatics research center Antwerp) is a new interdisciplinary research network for biomedical informatics and established by the University of Antwerp and the Antwerp University Hospital. It was established at the intersection of bioinformatics, medical informatics and translational medicine to address the growing needs in data handling and to enable new interdisciplinary cross-fertilization.