Research
AI-driven research at VIB.AI starts from biological questions and challenges that are addressed using state-of-the-art and novel computational and AI strategies. We combine in silico approaches with technology and wet lab experiments to arrive at an integrated research approach in a “humid” environment.
Our research lines cover multiple biological layers from the genome to cell function, tissues, and organisms and populations. Along the spectrum of computational modeling, we will focus on representation learning, AI-explainability, and hybrid models.
Biological applications of our research are equally broad: from microorganisms to plant biology, biodiversity and ecology, neuroscience, cancer, and immunology. We also welcome applicants with applied projects, including synthetic biology, AI-driven experiments (experiment-in-the-loop), or bio-engineering.
Further reading
VIB.AI has just been launched, but we build our research agenda on a strong foundation of computational expertise at VIB in diverse areas of life sciences. Here you find a small selection of recent output from members of the VIB.AI community.
Cell type directed design of synthetic enhancers
Ibrahim Ihsan Taskiran, Katina I. Spanier, Hannah Dickmänken, Niklas Kempynck, Alexandra Pančíková, Eren Can Ekşi, Gert Hulselmans, Joy N. Ismail, Koen Theunis, Roel Vandepoel, Valerie Christiaens, David Mauduit, Stein Aerts
An introduction to adversarially robust deep learning
Jonathan Peck, Bart Goossens, Yvan Saeys
Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice
Dajo Smet, Helder Opdebeeck, Klaas Vandepoele
Machine Learning Pipeline for Predicting Bone Marrow Edema Along the Sacroiliac Joints on Magnetic Resonance Imaging
Joris Roels, Ann-Sophie De Craemer, Thomas Renson, Manouk de Hooge, Arne Gevaert, Thomas Van Den Berghe, Lennart Jans, Nele Herregods, Philippe Carron, Filip Van den Bosch, Yvan Saeys, Dirk Elewaut
Decoding gene regulation in the fly brain
Jasper Janssens, Sara Aibar, Ibrahim Ihsan Taskiran, Joy N. Ismail, Alicia Estacio Gomez, Gabriel Aughey, Katina I. Spanier, Florian V. De Rop, Carmen Bravo González-Blas, Marc Dionne, Krista Grimes, Xiao Jiang Quan, Dafni Papasokrati, Gert Hulselmans, Samira Makhzami, Maxime De Waegeneer, Valerie Christiaens, Tony Southall, Stein Aerts
Energy efficient network activity from disparate circuit parameters
Deistler M, Macke JH*, Gonçalves PJ*
Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators
Inge De Clercq, Jan Van de Velde, Xiaopeng Luo, Li Liu, Veronique Storme, Michiel Van Bel, Robin Pottie, Dries Vaneechoutte, Frank Van Breusegem, Klaas Vandepoele
Benchmarking of cell type deconvolution pipelines for transcriptomics data
Francisco Avila Cobos, José Alquicira-Hernandez, Joseph E Powell, Pieter Mestdagh, Katleen De Preter