Friday, 29 October 2021 | 12:00 noon – Cape Town, SOUTH AFRICA
Professor of Electrical and Computer Engineering University of Nebraska–Lincoln, USA
Utilization of Bayesian Networks in Systems Biology
(Host: L. Zerbini
Bayesian Networks (BNs) are probabilistic graph representations that show the dependency structure between a set of random variables. BNs can be used to assess the fitness of data to a network, infer the value of a subset of nodes in a network given other nodes, and learn networks given data. We make use of these three features and use BNs to perform pathway enrichment analysis and to reconstruct interaction networks. In the former, we incorporate the topology of the pathways in the model to identify active pathways based on experimental data. In the latter, we incorporate external knowledge to optimize structure learning and identify interaction networks both for single and multi-omic data. Our results suggest that BNs are viable tools for omic data analysis as they capture both linear and nonlinear interactions, handle stochastic events in a probabilistic framework accounting for noise, and focus on local interactions, which can be related to causal inference.