Marcel J.T. Reinders is full professor at Delft University of Technology where he heads the Pattern Recognition and Bioinformatics group that conducts research into pattern recognition, computer vision and bioinformatics. Prof Reinders group is recognized as international experts on machine learning and in his bioinformatics research he applies cutting-edge machine learning expertise to develop data-driven analysis methodologies to progress molecular biology insights. Prof Reinders has a secondary appointment with the Leiden University Medical Center, Leiden, the Netherlands, where he heads a computational biology group that translates bioinformatics towards clinical applications. Prof Reinders initiated work on molecular classification and genetic network modelling. Nowadays he focuses on sequencing analysis tools, network-based analysis, and integration of genomic data. He has ample experience with finding gene signatures with applications in cancer and neurodegenerative diseases. He now also runs various projects on analyzing single cell data including spatial transcriptomics/CyTOF data.
Research Keywords & Expertise
Bioinformatics
Computational Biology
Computer Science
Human Genetics
machine learning
Short Biography
Marcel J.T. Reinders is full professor at Delft University of Technology where he heads the Pattern Recognition and Bioinformatics group that conducts research into pattern recognition, computer vision and bioinformatics. Prof Reinders group is recognized as international experts on machine learning and in his bioinformatics research he applies cutting-edge machine learning expertise to develop data-driven analysis methodologies to progress molecular biology insights. Prof Reinders has a secondary appointment with the Leiden University Medical Center, Leiden, the Netherlands, where he heads a computational biology group that translates bioinformatics towards clinical applications. Prof Reinders initiated work on molecular classification and genetic network modelling. Nowadays he focuses on sequencing analysis tools, network-based analysis, and integration of genomic data. He has ample experience with finding gene signatures with applications in cancer and neurodegenerative diseases. He now also runs various projects on analyzing single cell data including spatial transcriptomics/CyTOF data.