Frontiers of Science: Machine learning challenges for single-cell omics data
Prof. Yvan Saeys, Inflammation Research Center, VIB-UGent, Belgium
Machine learning challenges for single-cell omics data
Host: Tomi Suomi (firstname.lastname@example.org)
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.
A comparison of single-cell trajectory inference methods. Nature Biotechnology, 2019 (PMID 30936559)
Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nature Reviews Immunology, 2016 (PMID 27320317)
FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A, 2015 (PMID 25573116)