Yvan Saeys Lab

Research focus

​Our group studies the design and application of novel data mining and machine learning techniques, motivated by specific questions in biology and medicine.  To this end, our group combines the expertise of both strong analytical skills, exemplified by solid backgrounds in applied mathematics, computer science and engineering, with expertise in applied bioinformatics.

In the field of systems immunology, our group develops new computational approaches to unravel the regulatory landscape of immune cell differentiation and functioning.  High-throughput methods such as microarrays, next-generation-sequencing (NGS), multiplexed flow cytometry and imaging are currently revolutionizing the field of immunology, allowing us to study cells and their interactions into unprecedented depth.  While these technologies are able to generate massive amounts of data on cell behavior and functioning, interpreting these data and making sense out of it is currently the next challenge.  Our group develops novel systems biology approaches to model the regulatory landscape of immune cells using module networks and computational flow cytometry.

On the more algorithmic side, our research group develops new machine learning approaches to deal with challenging extensions of the classical learning paradigms, including high-dimensional, small sample settings, semi-supervised learning, imbalanced data, and structured input and output representations.  Our group has a solid expertise in the development of feature selection (biomarker selection) algorithms, and is currently exploring the potential of these techniques for new data types, such as flow cytometry and imaging.  A great part of our research effort also goes to the analysis of integrated “omics” approaches, where we take a network approach to integrate various data sources, and make use of novel graph mining approaches to formulate biological questions as machine learning questions on graphs.

In addition to our methodological research, we also aim to provide the scientific community with freely available, easy-to-use webtools, databases and other publicly available resources that result from our newly developed algorithms.  An overview of our tools can be found here.


Computational flow cytometry: helping to make sense of high-dimensional immunology dataSaeys Y, Van Gassen S, Lambrecht BNATURE REVIEWS IMMUNOLOGY, 16, 449-62, 2016
Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and SpeciesGuilliams M* Dutertre C Scott C Mcgovern N Sichien D Chakarov S Van Gassen S Chen J Poidinger M De Prijck S Tavernier S Low I Irac S Mattar C Sumatoh H Low G Chung T Chan D Tan K Hon T Fossum E Bogen B Choolani M Chan J Larbi A Luche H Henri S Saeys Y Newell E Lambrecht B* Malissen B* Ginhoux F*IMMUNITY, 45, 669-84, 2016* These authors contributed equally
FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry dataVan Gassen S, Callebaut B, van Helden M, Lambrecht B, Demeester P, Dhaene T, Saeys YCYTOMETRY PART A, 87, 636-45, 2015
Wisdom of crowds for robust gene network inferenceMarbach D, Costello J, Et al NATURE METHODS, 9, 796-804, 2012
Robust biomarker identification for cancer diagnosis with ensemble feature selection methodsAbeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys YBIOINFORMATICS, 26, 392-8, 2010


VIB scientists develop best methods for automated flow cytometry analysis

16/07/2016 - Flow cytometry has been widely used by immunologists and cancer biologists for more than 30 years as a biomedical research tool to distinguish different cell types in mixed populations based on the expression of cellular markers.

GenomeView wins 'Most Creative Visualization' Award

15/09/2011 - Thomas Abeel, VIB Department of Plant Systems Biology, UGent, is one of the winners in the first iDEA (Illumina’s Data Excellence Award) Challenge

VIB scientists win DREAM-5 for systems biology

18/03/2011 - The DREAM is the most important benchmark for the comparison of computational models used in systems biology. Among all the top labs, their test was unanimously rated the overall best performer based on a number of gold standard data sets.

Yvan Saeys

Yvan Saeys

Research area(s)


PhD: Ghent Univ., Ghent, Belgium, 2004
Postdoc: Univ. Claude Bernard, Lyon, France, 2009
Postdoc: Ghent Univ., Ghent, Belgium, 2009-2013
Professor: Ghent Univ., Ghent, Belgium, since 2015
VIB Group leader since 2015

Contact Info

VIB Center for Inflammation ResearchUGentUGent-VIB Research Building FSVMTechnologiepark 927 9052 GENTRoute description