Journal ArticleDOI
Combining functional and topological properties to identify core modules in protein interaction networks.
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TLDR
Two alternative network measures for analysis of PINs are described, which combine functional information with topological properties of the networks, and an algorithm for identification of functional modules, called SWEMODE, that identifies dense sub‐graphs containing functionally similar proteins.Abstract:
Advances in large-scale technologies in proteomics, such as yeast two-hybrid screening and mass spectrometry, have made it possible to generate large Protein Interaction Networks (PINs). Recent methods for identifying dense sub-graphs in such networks have been based solely on graph theoretic properties. Therefore, there is a need for an approach that will allow us to combine domain-specific knowledge with topological properties to generate functionally relevant sub-graphs from large networks. This article describes two alternative network measures for analysis of PINs, which combine functional information with topological properties of the networks. These measures, called weighted clustering coefficient and weighted average nearest-neighbors degree, use weights representing the strengths of interactions between the proteins, calculated according to their semantic similarity, which is based on the Gene Ontology terms of the proteins. We perform a global analysis of the yeast PIN by systematically comparing the weighted measures with their topological counterparts. To show the usefulness of the weighted measures, we develop an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The proposed method is based on the ranking of nodes, i.e., proteins, according to their weighted neighborhood cohesiveness. The highest ranked nodes are considered as seeds for candidate modules. The algorithm then iterates through the neighborhood of each seed protein, to identify densely connected proteins with high functional similarity, according to the chosen parameters. Using a yeast two-hybrid data set of experimentally determined protein-protein interactions, we demonstrate that SWEMODE is able to identify dense clusters containing proteins that are functionally similar. Many of the identified modules correspond to known complexes or subunits of these complexes.read more
Citations
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Journal ArticleDOI
Semantic similarity in biomedical ontologies.
TL;DR: This work reviews semantic similarity measures applied to biomedical ontologies and proposes their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise.
Journal ArticleDOI
Disease candidate gene identification and prioritization using protein interaction networks
Jing Chen,Jing Chen,Bruce J. Aronow,Bruce J. Aronow,Bruce J. Aronow,Anil G. Jegga,Anil G. Jegga +6 more
TL;DR: Extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema and it is demonstrated that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.
Journal ArticleDOI
Computational approaches for detecting protein complexes from protein interaction networks: a survey
TL;DR: The state-of-the-art techniques for computational detection of protein complexes are reviewed, some promising research directions in this field are discussed, and experimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes.
Journal ArticleDOI
Semantic integration to identify overlapping functional modules in protein interaction networks
TL;DR: A flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks and shows that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence.
Journal ArticleDOI
Identifying protein complexes and functional modules—from static PPI networks to dynamic PPI networks
TL;DR: Computational algorithms for identifying protein complexes and/or functional modules from protein-protein interaction (PPI) networks are reviewed.
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