Date/Time: 7th February 2017, 13:00 - 14:00

Venue: Level 2 Meeting Room, CBCB, Baddiley-Clark Building

CSBB Research Seminars

Abstract: Recent technological advances in single cell analysis are currently revolutionizing biological and sciences. In immunology, multicolor flow cytometry has been the major workhorse for throughput single cell analysis, and current innovations in the field, such as mass cytometry, characterising up to 50 parameters per cell, providing high-dimensional descriptions at single level. In parallel, single cell transcriptomics technologies have greatly matured during the last allowing now using highly multiplexed assays that measure whole transcriptomes at the cell level, and this for thousands of cells.

In this talk I will discuss trajectory inference, a novel class of machine learning methods that opens new opportunities for studying dynamic cellular processes based on single cell data.

Biography: Yvan Saeys obtained his M.Sc. (2000) and Ph.D. (2004) in Computer Science at Ghent University. He is currently associate Professor at Ghent University, and a Principal Investigator at the VIB where he is leading the DAMBI group (Data Mining and Modeling for Biomedicine) research focuses on the development and application of data mining and machine learning techniques for and medical applications.  He has over 100 publications in internationally renowned journals and is an associate editor of the journal Information Fusion.

A regular series of talks by members of the Centre to showcase current research, encourage collaboration and generate new ideas.  If you are not on the mailing list for the talks and would like to be, contact us.