Improving functional modules discovery by enriching interaction networks with gene profiles

Handle URI:
http://hdl.handle.net/10754/562753
Title:
Improving functional modules discovery by enriching interaction networks with gene profiles
Authors:
Salem, Saeed; Alroobi, Rami; Banitaan, Shadi; Seridi, Loqmane; Aljarah, Ibrahim; Brewer, James
Abstract:
Recent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Bentham Science Publishers
Journal:
Current Bioinformatics
Issue Date:
1-May-2013
DOI:
10.2174/1574893611308030008
Type:
Article
ISSN:
15748936
Sponsors:
We would like to thank the anonymous reviewers for their comments and suggestions. This publication was made possible by NIH grant number P20RR016471 from the INBRE program of the National Center for Research Resources.
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSalem, Saeeden
dc.contributor.authorAlroobi, Ramien
dc.contributor.authorBanitaan, Shadien
dc.contributor.authorSeridi, Loqmaneen
dc.contributor.authorAljarah, Ibrahimen
dc.contributor.authorBrewer, Jamesen
dc.date.accessioned2015-08-03T11:04:25Zen
dc.date.available2015-08-03T11:04:25Zen
dc.date.issued2013-05-01en
dc.identifier.issn15748936en
dc.identifier.doi10.2174/1574893611308030008en
dc.identifier.urihttp://hdl.handle.net/10754/562753en
dc.description.abstractRecent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.en
dc.description.sponsorshipWe would like to thank the anonymous reviewers for their comments and suggestions. This publication was made possible by NIH grant number P20RR016471 from the INBRE program of the National Center for Research Resources.en
dc.publisherBentham Science Publishersen
dc.titleImproving functional modules discovery by enriching interaction networks with gene profilesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalCurrent Bioinformaticsen
dc.contributor.institutionDepartment of Computer Science, North Dakota State University, Fargo, ND, United Statesen
kaust.authorSeridi, Loqmaneen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.