Single-cell SPACE, the next frontier

Unravelling cellular heterogeneity and dynamics in normal and malignant hematopoiesis using singlecell bioinformatics

An expert-guided pipeline for the analysis of spatial proteomics data

Comptuational modelling of cell-cell communication

Doctoral project David Novák

Doctoral project Helena Todorov

Doctoral project Katrien Quintelier

Doctoral project Laure Van Steenbergen

Doctoral project Lotte Pollaris

Doctoral project Louise Deconinck

Doctoral project Maxim Lippeveld

Explainable machine learning models for graph based neural networks

Improving diagnosis of primary immunodeficiencies using computational cytometry

Improving the robustness of deep neural networks to adversarial perturbations Improving the robustness of deep neural networks to adversarial perturbations

Machine learning based quantitative image analysis of correlative light and electron microscopy

Machine learning methods for the integration of spatial multi-omics data

Modeling intercellular communication from transcriptomics data by linking ligands to target genes.

Modelling intercellular communication in time and space

Modelling the heterogeneity of disease progression using trajectory inference

Optimizing computational cytometry tools for clinical applications

Towards explainable and explorable single-cell embeddings

Unifying Attribution-Based Explanation Methods in Machine Learning

Automated analysis of spatial biology data

Doctoral project Breght Vandenberghe

Doctoral project Céline Van Dender

Doctoral project Joeri Ruyssinck

Doctoral project Joris Roels

Doctoral project Milan Malfait

Doctoral project Robin Vandaele

Doctoral project Sofie Van Gassen

Mache learning applications based on fuzzy rough sets

Machine learning methods for single-cell transcriptomics

Methods for differential expression analysis in single-cell transcriptomics at the gene- and subgene level

A formal characterization of the robustness of deep learning techniques

A Formal Characterization of the Robustness of Deep Neural Networks to Adversarial Perturbations

Aligning patient specific cell development trajectories.

Alliance Research Group: Logic Engineering

An exploratory study of machine learning techniques for imaging flow cytometry

A quantitative evaluation of deep learning techniques for unsupervised and supervised analysis of high-dimensional cytometry data

Cell-cell cOmmuNicaTion As a driver of Cancer cell state identiTy - Decoding the impact of cell-cell communications on the identity of tumor states in skin cancers

Clinical applications of computational cytometry

Computational cytometry techniques for clinical applications

Dealing with Imbalanced and Weakly Labeled Data in Machine Learning using Fuzzy and Rough Set Methods

Digital innovation for man and society

Dissecting the regulatory dynamics that govern Kupffer cell differentiation

Dissecting tissue spatial organization using machine learning and spatial transcriptomics

Improving the Diagnosis and Prognosis of Primary Immunodeficiencies using Computational Flow Cytometry

Interpretable models for integrative single-cell spatial transcriptomics

Mining of large-scale single-cell data with deep learning and parallel multi-instance algorithms

Modelling intercellular communication in time and space

Modelling intercellular signaling events during cellular differentiation from single-cell transcriptomics data

Quantifying neural network interpretability: a benchmarking approach

TAILOR A Network of Research Excellence Centres

Trustworthy machine learning models for single-cell and spatial omics data

Unraveling the functional specialization of macrophages and dendritic cells using gene module networks and cell-cell signaling networks.

What makes our neurons human? Mechanisms linking human genomic and neuronal evolution, and how they relate to nature and nurture in health and disease

A human pluripotent stem cell derived differentiation model for the study of normal and transformed sympathoadrenal progenitors

FullPicture: Combining Spectral Flow Cytometry with High-Speed Cell Imaging to enable comprehensive profiling of plant and animal cells and their components in real time.

How T cell autoreactivity develops in early rheumatoid arthritis: the smoking gun hypothesis

‘Immunodeficiency-causing IKAROS mutations in the bright sunlight’, A comprehensive study characterizing the genetic and immunological heterogeneity in IKAROS-mediated immune diseases.

Multi-labeled semi-supervised learning in big data problem

SpatialConnect: linking tissue biology to the new era of single-cell spatial transcriptomics

The BD FACS Symphony FACS cell sorter: the most advanced multiparameter fluorescence activated cell sorter that allows the identification and purification of rare cells for further functional characterization

Understanding the role of nuclear receptor HNF4a in sepsis and unravel the mechanisms of its loss-of-function in relation to PPARa failure.

Unraveling the choroid plexus heterogeneity and its consequences on the biological response to inflammation.

Unraveling the regulatory landscape of dendritic cells using module networks.

An exploratory study of feature selection techniques for unsupervised learning

CONDITIONAL: COvid-19: iNterplay between genetic preDisposITION and immunologicAl Landscape

Covid-Track: applying quantitative virology and single-cell genomics to unravel the virus-host interactions leading to severe immunopathologies in COVID-19 patients

Role of intestinal barrier dysfunction for the pathogenesis of spondyloarthritis

Understanding the immunological basis of persistent airway obstruction in asthma