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|Title:||FUSE : a profit maximization approach for functional sum-marization of biological networks||Authors:||Seah, Boon-Siew
Dewey Jr., C. Forbes
Bhowmick, Sourav S.
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2012||Source:||Seah, B.-S., Bhowmick, S. S., Dewey Jr, C. F., & Yu, H. (2012). FUSE: a profit maximization approach for functional summarization of biological networks. BMC Bioinformatics, 13.||Series/Report no.:||BMC bioinformatics||Abstract:||Background: 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. 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, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. Results: We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. Conclusion: 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. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.||URI:||https://hdl.handle.net/10356/95888
|DOI:||10.1186/1471-2105-13-S3-S10||Rights:||© 2012 The Authors. This paper was published in BMC Bioinformatics and is made available as an electronic reprint (preprint) with permission of The Authors. The paper can be found at the following official DOI: [http://dx.doi.org/10.1186/1471-2105-13-S3-S10]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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