Optimizing network propagation for multi-omics data integration.

ABSTRACT: Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a 'topology bi

SUBMITTER: Charmpi K 

PROVIDER: S-EPMC8664198 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature