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dc.contributor.authorLiu, Mei
dc.contributor.authorChen, Xue-wen
dc.contributor.authorJothi, Raja
dc.date.accessioned2014-04-16T15:33:36Z
dc.date.available2014-04-16T15:33:36Z
dc.date.issued2009-08-10
dc.identifier.citationLiu, Mei, Xue-wen Chen, and Raja Jothi. 2009. “Knowledge-Guided Inference of Domain-Domain Interactions from Incomplete Protein-Protein Interaction Networks.” Bioinformatics 25 (19): 2492–99. http://dx.doi.org/10.1093/bioinformatics/btp480
dc.identifier.urihttp://hdl.handle.net/1808/13526
dc.descriptionPlease note: This article is available free of charge from Pub Med Central at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2701933
dc.description.abstractMotivation: Protein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks; (ii) precise identification of binding sites; (iii) acquisition of insights into the causes of deleterious mutations at interaction sites; and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain–domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs.

Results: Here, we describe a novel method called K-GIDDI (knowledge-guided inference of DDIs) to narrow down the PPI sites to smaller regions/domains. K-GIDDI constructs an initial DDI network from cross-species PPI networks, and then expands the DDI network by inferring additional DDIs using a divide-and-conquer biclustering algorithm guided by Gene Ontology (GO) information, which identifies partial-complete bipartite sub-networks in the DDI network and makes them complete bipartite sub-networks by adding edges. Our results indicate that K-GIDDI can reliably predict DDIs. Most importantly, K-GIDDI's novel network expansion procedure allows prediction of DDIs that are otherwise not identifiable by methods that rely only on PPI data.
dc.description.sponsorshipNational Science Foundation (Award IIS-0644366 to X-W.C.); Intramural Research Program of the National Institutes of Health, NIEHS (to R.J.).
dc.publisherOxford University Press
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by-nc/2.0/uk/
dc.titleKnowledge-guided inference of domain–domain interactions from incomplete protein–protein interaction networks
dc.typeArticle
kusw.kuauthorLiu, Mei
kusw.kuauthorChen, Xue-wen
kusw.kudepartmentDepartment of Electrical Engineering and Computer Science
kusw.oastatusfullparticipation
dc.identifier.doi10.1093/bioinformatics/btp480
dc.identifier.orcidhttps://orcid.org/0000-0002-8036-2110
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as: This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.