Graphlet-based edge clustering reveals pathogen-interacting proteins
Prediction of protein function from protein interaction networks has received attention in the post-genomic era. A popular strategy has been to cluster the network into functionally coherent groups of proteins and assign the entire cluster with a function based on functions of its annotated members....
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| Published in: | Bioinformatics Vol. 28; no. 18; pp. i480 - i486 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Oxford University Press
15-09-2012
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Prediction of protein function from protein interaction networks has received attention in the post-genomic era. A popular strategy has been to cluster the network into functionally coherent groups of proteins and assign the entire cluster with a function based on functions of its annotated members. Traditionally, network research has focused on clustering of nodes. However, clustering of edges may be preferred: nodes belong to multiple functional groups, but clustering of nodes typically cannot capture the group overlap, while clustering of edges can. Clustering of adjacent edges that share many neighbors was proposed recently, outperforming different node clustering methods. However, since some biological processes can have characteristic 'signatures' throughout the network, not just locally, it may be of interest to consider edges that are not necessarily adjacent.
We design a sensitive measure of the 'topological similarity' of edges that can deal with edges that are not necessarily adjacent. We cluster edges that are similar according to our measure in different baker's yeast protein interaction networks, outperforming existing node and edge clustering approaches. We apply our approach to the human network to predict new pathogen-interacting proteins. This is important, since these proteins represent drug target candidates.
Software executables are freely available upon request.
tmilenko@nd.edu
Supplementary data are available at Bioinformatics online. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1367-4803 1367-4811 1460-2059 |
| DOI: | 10.1093/bioinformatics/bts376 |