Exploitation of genetic interaction network topology for the prediction of epistatic behavior

Handle URI:
http://hdl.handle.net/10754/563027
Title:
Exploitation of genetic interaction network topology for the prediction of epistatic behavior
Authors:
Alanis Lobato, Gregorio ( 0000-0001-9339-4229 ) ; Cannistraci, Carlo; Ravasi, Timothy ( 0000-0002-9950-465X )
Abstract:
Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Integrative Systems Biology Lab; Bioscience Program; Computer Science Program
Publisher:
Elsevier BV
Journal:
Genomics
Issue Date:
Oct-2013
DOI:
10.1016/j.ygeno.2013.07.010
PubMed ID:
23892246
Type:
Article
ISSN:
08887543
Appears in Collections:
Articles; Bioscience Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlanis Lobato, Gregorioen
dc.contributor.authorCannistraci, Carloen
dc.contributor.authorRavasi, Timothyen
dc.date.accessioned2015-08-03T11:34:02Zen
dc.date.available2015-08-03T11:34:02Zen
dc.date.issued2013-10en
dc.identifier.issn08887543en
dc.identifier.pmid23892246en
dc.identifier.doi10.1016/j.ygeno.2013.07.010en
dc.identifier.urihttp://hdl.handle.net/10754/563027en
dc.description.abstractGenetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.en
dc.publisherElsevier BVen
dc.subjectComputational biologyen
dc.subjectGene networksen
dc.subjectGenetic epistasisen
dc.subjectProjections and predictionsen
dc.subjectSystems biologyen
dc.titleExploitation of genetic interaction network topology for the prediction of epistatic behavioren
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentIntegrative Systems Biology Laben
dc.contributor.departmentBioscience Programen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalGenomicsen
dc.contributor.institutionDivision of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United Statesen
kaust.authorAlanis Lobato, Gregorioen
kaust.authorCannistraci, Carloen
kaust.authorRavasi, Timothyen

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