Please use this identifier to cite or link to this item: https://doi.org/10.1145/2147805.2147806
Title: FUSE: Towards multi-level functional summarization of protein interaction networks
Authors: Seah, B.-S.
Bhowmick, S.S.
Dewey Jr., C.F.
Yu, H. 
Keywords: Functional clusters
Functional summarization
Graph summarization
Protein interaction network
Issue Date: 2011
Citation: Seah, B.-S.,Bhowmick, S.S.,Dewey Jr., C.F.,Yu, H. (2011). FUSE: Towards multi-level functional summarization of protein interaction networks. 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011 : 2-11. ScholarBank@NUS Repository. https://doi.org/10.1145/2147805.2147806
Abstract: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (ppi) using graph theoretic analysis. Despite the recent progress, systems level analysis of ppis remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize ppis at multiple resolutions to provide high level views of its functional landscape are still lacking. In this paper, we present a novel data-driven and generic algorithm called fuse (Functional Summary Generator) that generates functional maps of a ppi at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions. By simultaneously evaluating interaction and annotation data, fuse abstracts higher-order interaction maps by reducing the details of the underlying ppi to form a functional summary graph of interconnected functional clusters. To this end, fuse exploits Minimum Description Length (mdl) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. Extensive experiments on real-world ppis demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with go term enrichment. Copyright © 2011 ACM.
Source Title: 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011
URI: http://scholarbank.nus.edu.sg/handle/10635/115427
ISBN: 9781450307963
DOI: 10.1145/2147805.2147806
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