Session 1

8:30 - 8:40
 

Opening remarks
 

8:40 - 8:50
 

Yazdan Asgari. Paris-Saclay University, Paris-Sud University, Gustave Roussy, Villejuif, France.
Developing a new pipeline for exploring pleiotropy of GWAS data at gene-level.

8:50 - 9:30
 
 

Keynote talk: Matthieu Foll. International Agency for Research on Cancer, Lyon, France.
Multi-omics characterization of rare heterogeneous tumors.
 

9:30 - 10:00
 

Coffee Break
 

10:00 - 10:10
 

Vaishali Waman. University College London, London, United Kingdom.
Predicting human and viral protein variants affecting COVID-19 susceptibility.

10:10 - 10:30
 

Kivilcim Ozturk. University of California San Diego, La Jolla (CA), USA.
Interface-guided phenotyping of coding variants.

10:30 - 10:40
 
 

Swatantra Pradhan. Tata Consultancy Services, India.
An augmented transformer model trained on family specific variant data leads to improved prediction of variants
of uncertain significance.

10:40 - 10:50
 
 

Nigreisy Montalvo. Imagine Institute, Paris, France.
Pathogenicity scoring of genetic variants through federated learning across independent institutions reaches
comparable or superior performance than the centralized-data model counterparts.

10:50 - 11:00
 

Julian Gough. MRC Laboratory of Molecular Biology, United Kingdom.
Hypothesis-free phenotype prediction within a genetics-first framework.

11:00 - 11:20
 

Alexander Schoenhuth. Bielefeld University, Germany.
Predicting the prevalence of complex genetic diseases from individual genotype profiles using capsule networks.

11:20 - 12:00
 
 

Keynote talk: Jörg Menche. University of Vienna, Austria
Network Medicine – From protein-protein to human-machine interactions.
 

12:00 - 13:20
 

Lunch break
 

Session 2

13:20 - 13:40
 

Rongting Huang. The University of Hong Kong, Hong Kong.
XClone: detection of allele-specific subclonal copy number variations from single-cell transcriptomic data.

13:40 - 14:00
 
 

David Wang. University of Pennsylvania, Philadelphia (PA) USA.
Comprehensive Identification and Characterization of Splicing Associated Variants with Coverage Aware
Statistical Models.

14:00 - 14:10
 
 

Nina Baumgarten. Goethe University, Frankfurt am Main, Germany.
A statistical approach to identify regulatory DNA variations combined with epigenomics data reveals novel
non-coding disease genes.

14:10 - 14:20
 

Eleni Giannoulatou. Victor Chang Cardiac Research Institute, Sydney (NSW), Australia.
SpliceSM: machine learning discovery of splice-altering variants using susceptibility maps.

14:20 - 15:00
 

Keynote talk: Nicky Whiffin. University of Oxford, Oxford, United Kingdom.
Strategies to annotate and interpret non-coding variants in rare disease.

15:00 - 15:30
 

Coffee Break
 

15:30 - 15:50
 
 

Proceedings Talk: Yasser Mohseni Behbahani. Sorbonne Université, Paris, France.
Deep Local Analysis deconstructs protein - protein interfaces and accurately estimates binding affinity
changes upon mutation.

15:50 - 16:10
 
 

Jayoung Ryu. Harvard Medical School, Boston (MA), USA.
Modeling endogenous editing outcome of base editor reporter screens with CRISPR-Bean discovers causal
variants for cellular LDL uptake.

16:10 - 16:20
 
 

Daniele Raimondi. KU Leuven, Leuven, Belgium.
From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes
from sequencing data.

16:20 - 16:30

Closing remarks.