Abstract
BackgroundAnalyzing interaction networks for functional characterization poses significant challenges arising from the noisy, incomplete, and generic nature of both the interaction data as well as functional annotation of molecules. Network-based methods focus on interacting molecules (pairs or sets) occurring in close proximity to infer functional associations.ResultsIn this paper we perform a formal comparative investigation of the relationship between functional coherence and topological proximity in networks. We investigate the problem of assessing the coherence of sets of biomolecules (or segments thereof) taking into account functional specificity as well as the distribution of functional attributes across entity groups. We also propose novel measures of topological proximity that are more robust to noisy and incomplete interaction data.ConclusionWe derive the following results in this paper: (i) there exists strong correlation between functional similarity and topological proximity in various network abstractions, with domain interaction networks (DDIs) demonstrating higher correlation than protein interaction networks (PPIs); (ii) measures that quantify coherence among entire sets of proteins are superior to aggregates of known pair-wise measures; and (iii) random-walk based measures of topological proximity are better suited to existing interaction data. We validate our methods on diverse data, including experimentally and computationally derived PPIs and DDIs, as well as on sets of known biologically related groups of molecules.
Highlights
Analyzing interaction networks for functional characterization poses significant challenges arising from the noisy, incomplete, and generic nature of both the interaction data as well as functional annotation of molecules
We comprehensively investigate the relationship between topological and functional modularity in the context of two network abstractions protein-protein interaction (PPI) and domain-domain interaction (DDI) networks
We have shown that information-theoretic measures that are designed to address these challenges are effective in capturing the relationship between the functional coherence and network proximity of pairs of proteins [9]
Summary
Analyzing interaction networks for functional characterization poses significant challenges arising from the noisy, incomplete, and generic nature of both the interaction data as well as functional annotation of molecules. Analysis of interaction data generated from high throughput experiments takes a network-centric view of functions of biological systems and the role of the underlying components. Recent advances in this area have focused on the development of computational tools for network-based functional annotation [1], identification of functionally coherent modules [2], and relationship between network structure and function [3,4], among others. Key to understanding the relationship between network topology and functional modularity are: (i) suitable measures for assessing the functional coherence (or similarity) of a group of entities with respect to each other, and (ii) measures for quantifying the topological proximity in a network with potential missing interactions and noise. We build upon existing methods for quantifying functional coherence and topological proximity through the following key results:
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