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Resolving the Structure of Interactomes with Hierarchical Agglomerative Clustering by Yongjin Park; Joel S Bader is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Resolving the Structure of Interactomes with Hierarchical Agglomerative Clustering about?

## Background Graphs provide a natural framework for visualizing and analyzing networks of many types, including biological networks. Network clustering is a valuable approach for summarizing the structure in large networks, for predicting unobserved interactions, and for predicting functional annotations. Many current clustering algorithms suffer from a common set of limitations: poor resolution of top-level clusters; over-splitting of bottom-level clusters; requirements to pre-define the number of clusters prior to analysis; and an inability to jointly cluster over multiple interaction types. ## Results A new algorithm, Hierarchical Agglomerative Clustering (HAC), is developed for fast clustering of heterogeneous interaction networks. This algorithm uses maximum likelihood to drive the inference of a hierarchical stochastic block model for network structure. Bayesian model selection provides a principled method for collapsing the fine-structure within the smallest groups, and for identifying the top-level groups within a network. Model scores are additive over independent interaction types, providing a direct route for simultaneous analysis of multiple interaction types. In addit

Who reads Resolving the Structure of Interactomes with Hierarchical Agglomerative Clustering?

Author

Yongjin Park; Joel S Bader

Publisher

BioMed Central; Springer (Biomed Central Ltd.); [London]: BioMed Central, [2000]-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1471-2105)

Published

2011

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)