Deep familial phenotyping and genotyping to resolve phenotypic variability of inherited pathogenic genetic variants KU Leuven
Organoid painting: Unbiased cellular phenotyping of human tissue mimics using deep learning-enhanced imaging and analysis. University of Antwerp
PRISMA:Polygenic Risc scoring and deep-Immunophenotyping Strategy to Master Alzheimer's disease KU Leuven
The consortium is committed to pushing Alzheimer's disease (AD) towards personalized medicine. This project will broaden and deepen the knowledge about genetic risk and immune biomarkers for AD. She will also build a solid data platform that can be used to rigorously recruit stratified populations for clinical studies and serve as a basis for designing personalized diagnostic applications and targeted interventions for AD. The central ...
An integrated translational platform to improve the management and outcome of rare heritable connective tissue disease Ghent University
This interdisciplinary project aims to improve the outcome of heritable connective tissue disease. Using deep phenotyping techniques in combination with advanced genetic analysis, both in the clinic and in animal models, we expect to uncover molecular mechanisms which will inform better disease management strategies. In parallel, we aim to identify novel therapeutic targets using unbiased phenotypic screening in zebrafish models.
Chromatin structure has been shown to play important roles in the orchestration of gene expression programs during development. Spatio-temporal specific cis-regulatory sequences often lie at a long distance from the gene(s) they regulate, requiring spatial chromatin folding to move them in close proximity of their target promoters and fine-tune the time, place and level of gene expression. Interestingly, several whole-genome chromatin ...
Dissecting tissue spatial organization using machine learning and spatial transcriptomics Ghent University
In this project, we aim to better functionally characterize different spatial contexts within tissues. To this end we will develop novel bioinformatics pipelines to process and integrate several U+201ComicsU+201D and imaging data types. Novel machine learning methods will be explored that aim to combine the high spatial resolution of imaging techniques with the deep phenotyping capabilities of current scRNAseq methods.
The objective of this research is to address the changes in the proteome of S. pastorianus required to exhibit a flocculation phenotype. For this, the proteome along fermentation of beer as well as over several re-pitching cycles will be quantified. Key kinases, characterized by changing their abundancy at the advent of flocculation, will be further investigated for a direct involvement in flocculation. This will give novel and fundamental ...