prof. Yvan Saeys (PhD)

CRIG group leader
Yvan Saeys

Principal Investigator - Data Mining and Modelling for Biomedicine Group, Inflammation Research Center, VIB
Associate Professor (Faculty of Medicine and Health Sciences, UGent)
Partner (Bioinformatics Institute Ghent)

 

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 cancer immunology, our group develops new computational approaches to unravel the regulatory landscape of cell differentiation and functioning.  High-throughput methods such as microarrays, next-generation-sequencing (NGS), multiplexed flow cytometry and imaging are currently revolutionizing the field, 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 as well as biomarker detection techniques using advanced types of module networks, computational flow cytometry and single cell "omics" technologies.
 

Biography

Yvan Saeys is associate professor of Machine Learning and Systems Immunology at VIB and Ghent University.  He is developing state-of-the-art data mining and machine learning methods for biological and medical applications, and is an expert in computational models to analyse high-throughput single-cell data.  The methods he develops have been shown to outperform competing techniques, including computational techniques for regulatory network inference (best performing team at the DREAM5 challenge) and biomarker discovery from high-throughput, single cell data (best performing team at the FlowCAP-IV challenge).  Yvan Saeys has published >100 papers in top ranking journals and conferences, ranging from methodological development in machine learning and bioinformatics to applications in cancer, immunology and medicine (Nature Immunology, Nature Methods, PNAS, Bioinformatics).  The tools he develops have received several awards and are being used by international consortia.  His work has been cited more than 5000 times.  The Saeys lab provide expertise in cancer data mining, flow cytometry bioinformatics for leukaemia and lymphoma, and general biomarker discovery and systems approaches to cancer research.
 

Research team

  •     prof. Pieter De Bleser (PhD) - post-doctoral fellow
  •     dr. Viacheslav Mylka (PhD) - post-doctoral fellow
  •     dr. Daniel Peralta (PhD) - post-doctoral fellow
  •     dr. Ruth Seurinck (PhD) - post-doctoral fellow
  •     dr. Niels Vandamme (PhD) - post-doctoral fellow
  •     dr. Sofie Van Gassen (PhD) - post-doctoral fellow
  •     Robin Browaeys - doctoral fellow
  •     Robrecht Cannoodt - doctoral fellow
  •     Louise Deconinck - doctoral fellow
  •     Annelies Emmaneel - doctoral fellow
  •     Arne Gevaert - doctoral fellow
  •     Maxim Lippeveld - doctoral fellow
  •     Jonathan Peck - doctoral fellow
  •     Katrien Quintelier - doctoral fellow
  •     Joris Roels - doctoral fellow
  •     Quentin Rouchon - doctoral fellow
  •     Wouter Saelens - doctoral fellow
  •     Helena Todorov - doctoral fellow
  •     Robin Vandaele - doctoral fellow
  •     Artuur Couckuyt - doctoral fellow
  •     Arne Soete - bioinformatician
  •     Caroline Vandenbulcke - bioinformatician/lab technician
  •     Kevin Verstaen - bioinformatician
  •     Jana Roels - bioinformatician
     

Key publications

  • A comparison of single-cell trajectory inference methods. Wouter Saelens (UGent) , Robrecht Cannoodt (UGent) , Helena Todorov (UGent) and Yvan Saeys (UGent) (2019) NATURE BIOTECHNOLOGY. 37(5). p.547-554 (PMID: 30936559)
  • A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Hannah Van Hove, Liesbet Martens (UGent) , Isabelle Scheyltjens, Karen De Vlaminck, Ana Rita Pombo Antunes, Sofie De Prijck (UGent) , Niels Vandamme (UGent) , Sebastiaan De Schepper, Gert Van Isterdael (UGent) , Charlotte Scott (UGent) , et al. (2019) NATURE NEUROSCIENCE. 22(6). p.1021-1035 (PMID: 31061494)
  • Single-Cell RNA Sequencing of the T Helper Cell Response to House Dust Mites Defines a Distinct Gene Expression Signature in Airway Th2 Cells. Tibbitt CA, Stark JM, Martens L, Ma J, Mold JE, Deswarte K, Oliynyk G, Feng X, Lambrecht BN, De Bleser P, Nylén S, Hammad H, Arsenian Henriksson M, Saeys Y, Coquet JM. Immunity. 2019 Jul 16;51(1):169-184.e5. doi: 10.1016/j.immuni.2019.05.014. Epub 2019 Jun 20. (PMID: 31231035) 
  • Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nature Reviews Immunology, 2016 (PMID: 27320317)
  • A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A, 2016 (PMID: 26447924)
  • FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.  Cytometry A, 2015 (PMID: 25573116)
  • Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics, 2010 (PMID: 19942583)
  • A review of feature selection techniques in bioinformatics. Bioinformatics, 2007 (PMID: 17720704)
     

Contact & links

  • Technologiepark 927, B-9052 Gent, Belgium
  • Dambi