Machine learning and single-cell bioinformatics
Our research is situated at the intersection of computer science and biomedical research, with a strong emphasis on the design and application of data mining and machine learning techniques.
We perform fundamental research on robustness and interpretability of machine learning/AI models, while at the same time making sure that our models focus on important challenges in biomedicine and clinical sciences. Many of our applications deal with single-cell technologies, including cytometry data, single-cell (multi)omics, and single-cell imaging data types. Clinical applications include allergies and asthma, rheumatology, cancer (lung cancer and leukemia) and primary immune deficiencies (PID).
Areas of Expertise
- Machine learning/Artificial Intelligence
- Single-cell and spatial omics
- Systems immunology
- Large-scale data mining and high-throughput biology
Technology Transfer Potential
- Clinical applications using computational flow cytometry
- Single-cell bioinformatics
- Spatial (multi)-omics
Selected publications
- Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods 17, 159-162 (2020). Visit ➚
- Quintelier, K. et al. Analyzing high-dimensional cytometry data using FlowSOM. Nat Protoc 16, 3775-3801 (2021). Visit ➚
- Cannoodt, R., Saelens, W., Deconinck, L. & Saeys, Y. Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells. Nat Commun 12, 3942 (2021). Visit ➚
- Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat Biotechnol 37, 547-554 (2019). Visit ➚
- Van Gassen, S. et al. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytom. Part A 87A, 636-645 (2015). Visit ➚
Bibliography
- Full bibliography Visit ➚
