Seminar Abstract

“Probabilistic Graphical Models for Small-sample Network Inference”

Probabilistic graphical models provide a powerful tool to efficiently model and analyze the conditional dependency relations among large sets of random variables. The correct identification of such relations plays a critical role when trying to obtain both quantitative and qualitative conclusions on the behavior of the modeled systems. This is more so in the context of small sample regimes that frequently characterize many problems in bioinformatics, such as the reconstruction of gene regulatory networks from microarray data. In this talk, we will present a general overview of some of the approaches that have dominated the literature during the last few years and we will sketch some new ideas regarding work currently in progress that is intended to overcome some of their limitations.