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(IB)2 Research Day 2018

16 November 2018

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.

Details

Date:
16 November 2018
Website:
https://goo.gl/forms/bDFUQyXp8R6baKIQ2

Venue

Groene Zaal, Congrescentrum U-Residence
VUB Campus, Generaal Jacqueslaan 271
Brussel, 1050 Belgique
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