Graphlet Laplacians: graphlet-based neighbourhoods highlight topology-function and topology-disease relationships

Abstract excerpt

<h4>Motivation</h4> Laplacian matrices capture the global structure of networks and are widely used to study biological networks. However, the local structure of the network around a node can also capture biological information. Local wiring patterns are typically quantified by counting how often a node touches different graphlets (small, connected, induced sub-graphs). Currently available graphlet-based methods...

Topics

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• Area Under Curve
• Cluster Analysis
• Computational Biology
• Gene Ontology
• Genetic Predisposition to Disease
• Models, Genetic
• Protein Interaction Maps
• ROC Curve

Identifiers and source

Literature Corpus work

f2355fa6-1c9c-5515-b873-b7e5a484564e

DOI

10.1101/460964