Abstract

Networks are ubiquitous in biology, thanks to their semantic value and transparency in representing systems of interactions. Although there is a rich tradition of using networks to represent gene associations and co-regulations, statistical approaches are often lacking a formal definition of what the links in the network represent. In this talk, I will show how probabilistic graphical models can be a formal way of representing gene conditional dependencies, and how the use of single-cell RNA-seq data to learn such dependencies can lead to novel biological insights.

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