Frontiers of Science: Machine learning challenges for single-cell omics data

Aika

10.10.2019 klo 12.00 - 13.00
Prof. Yvan Saeys, Inflammation Research Center, VIB-UGent, Belgium
Machine learning challenges for single-cell omics data

Host: Tomi Suomi (tomi.suomi@utu.fi)

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 180 papers in top ranking journals and conferences, ranging from methodological development in machine learning and bioinformatics to applications in cancer, immunology and medicine.


Jokilehto, Terhi