{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T14:46:47Z","timestamp":1770043607137,"version":"3.49.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2016,10,1]],"date-time":"2016-10-01T00:00:00Z","timestamp":1475280000000},"content-version":"vor","delay-in-days":2609,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: 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\u2013domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Contact: \u00a0xwchen@ku.edu<\/jats:p>\n               <jats:p>Availability: \u00a0http:\/\/www.ittc.ku.edu\/\u223cxwchen\/domainNetwork\/ddinet.html<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp480","type":"journal-article","created":{"date-parts":[[2009,8,11]],"date-time":"2009-08-11T01:06:19Z","timestamp":1249952779000},"page":"2492-2499","source":"Crossref","is-referenced-by-count":22,"title":["Knowledge-guided inference of domain\u2013domain interactions from incomplete protein\u2013protein interaction networks"],"prefix":"10.1093","volume":"25","author":[{"given":"Mei","family":"Liu","sequence":"first","affiliation":[{"name":"1 Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045 and 2Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA"}]},{"given":"Xue-wen","family":"Chen","sequence":"additional","affiliation":[{"name":"1 Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045 and 2Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA"}]},{"given":"Raja","family":"Jothi","sequence":"additional","affiliation":[{"name":"1 Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045 and 2Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA"}]}],"member":"286","published-online":{"date-parts":[[2009,8,10]]},"reference":[{"key":"2023013112123099900_B1","doi-asserted-by":"crossref","first-page":"13292","DOI":"10.1073\/pnas.0801207105","article-title":"Built-in loops allow versatility in domain-domain interactions: lessons from self-interacting domains","volume":"105","author":"Akiva","year":"2008","journal-title":"Proc. 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