Heterogeneity in cancer is a consequence of both irreversible genetic changes and reversible transcriptional changes. Melanoma is prime example for this since on the genomic level melanomas have a high mutational burden leading to inter and intra-tumor heterogeneity while on the transcriptomic level they can...
In the era of the rise against statistical significance: what have we (not) learnt from millions of scientific papers with P-values
The diversity of repertoires of B-cell and T-cell receptors is generated by a stochastic process of gene rearrangement called VDJ recombination, and is later sculpted by selection, clonal proliferation, and somatic hypermutations. I will show how these processes can be learned quantitatively from high-throughput repertoire...
pBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion.
Inverse problem : an application in brain imaging and in cancerology Inverse problem is a class of models in which we try to determine the causes of a phenomenon from the experimental observations of its effects. During my presentation, I will speak about two inverse...
Event Timing: October 25th, 2019 from 9am to 5.30pm Event Address: Bozar, Koningsstraat 10, 1000 Brussels (Entrance in front of the Parc Royal) Contact: Sophie de Buyl <sophie.de.Buyl@vub.be> Matthieu Defrance <matthieu.dc.defrance@ulb.ac.be> 9:00 - 9:25 Welcome Coffee 9:25 - 9:30 Introduction 9:30 - 10:30 Keynote 1...
Simon Dellicour, ULB Landscape phylogeography - Using viral gene sequences to compare and explain the heterogeneous spatial dynamics of virus epidemics
An interpretable semi-supervised classifier with bioinformatics applications. Isel Grau (VUB) In the context of medical or bioinformatic applications of machine learning, obtaining data instances is a relatively easy process but labeling them could become quite expensive or tedious. Such scenarios lead to datasets with few...