9.30 – 9.35 Welcome
9.35 – 10.35 Keynote 1 – Carl Herrmann, University of Heidelberg
Multi-omics data integration for single-cell genomics
10.35 – 11.05 Charlotte Nachtegael, Unraveling the oligogenic potential
of developmental disorders
IB2, ULB
11.05 – 11.30 Coffee Break
11.30 – 12.00 Youssef Bouysran, Bioinformatic investigations of missense mutations
in the FBN1 gene
IB2, ULB
12.00 – 12.30 Nathaniel Mon Père, Quantitative Models for Cell Differentiation in Hematopoiesis
IB2, ULB
12.30 – 12.45 Flash presentations
12.45 – 14.00 Lunch break and poster session
14.00 – 15.00 Keynote 2 – Hervé Isambert, Institut Curie Paris
Learning causal and non-causal networks from large scale genomic and
clinical data (Abstract below)
15.00 – 15.30 Gipsi Lima Mendez, Ocean Eco-system biology through integration
of heterogeneous data
IB2, ULB
15.30 – 16.00 Closing remarks / Future of the (IB)2
16.00 17.00 Musical Coffee
(IB)² – Interuniversity Institute of Bioinformatics in Brussels http://ibsquare.be
Contact: Sophie de Buyl sophie.de.Buyl at vub.be, Matthieu Defrance matthieu.dc.defrance at ulb.ac.be
Learning causal and non-causal networks from large scale genomic and clinical data, Hervé Issambert, Curie Institute
Network reconstruction aims at disentangling direct from indirect dependences in information-rich data and has become ubiquitous to analyze the rapidly expanding resources of genomic and clinical data. However, most network inference methods are restricted to specific types of data and assume either causal or non-causal graphical models a priori. We have developed an information-based approach, which reconstructs causal, non-causal or mixed networks from large scale genomic or clinical data, without the need for an a priori choice on the causal or non-causal nature of reconstructed networks. Starting from a fully connected graph, it first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of nodes. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. This computational approach outperforms or matches state-of-the-art methods for either causal (eg regulatory interaction) or non-causal (eg protein contact map) network reconstruction. In the talk, I will present different applications on a broad range of biological and clinical data, from single-cell transcriptomics and genomic alterations in tumor progression to long term evolution of vertebrates through whole genome duplication.